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xxxi, 338 p. : 23 cm

Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be, driven by a quest to progress from perceptions to measurements. Pursuit of this aim has led to brilliant successes. But alongside the successes stand problems whose solutions are not in sight. Representative of such problems is the problem of automation of driving in city traffic. In this case, as in many others, what can be done with ease by humans - without any measurements and a computations - is an intractable task for machines.

This paper is focused at providing a solution to optimal power flow problem in power systems by using soft computing approaches. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost.   Soft computing optimization methods have been implemented based on genetic algorithm and particle swarm optimization. The proposed soft computing techniques are modelled to be flexible for implementation to any power systems with the given system line,    bus    data,    generator fuel    cost parameter and forecasted load demand. Proposed soft computing optimization techniques have been analyzed and tested on the standard benchmark IEEE 30-bus system. Results obtained after applying both optimization techniques on American Electric IEEE 30-bus system with the same control variable maximum & minim um limits and system data have been compared and analyzed.   Proposed methods efficiently optimize and solve the optimal power flow problem with high efficiency and wide flexibility for implementation and analysis on different power system networks.

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.  by Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal

A thesis submitted as partial fulfillment for the degree of Bachelor of Science in Electronic and Telecommunications Engineering

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.

A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.

xv, 446 p. : 25 cm

International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

This paper is focused at providing a solution to optimal power flow problem in power systems by using soft computing approaches. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost.   Soft computing optimization methods have been implemented based on genetic algorithm and particle swarm optimization. The proposed soft computing techniques are modelled to be flexible for implementation to any power systems with the given system line,    bus    data,    generator fuel    cost parameter and forecasted load demand. Proposed soft computing optimization techniques have been analyzed and tested on the standard benchmark IEEE 30-bus system. Results obtained after applying both optimization techniques on American Electric IEEE 30-bus system with the same control variable maximum & minimum limits and system data have been compared and analyzed.   Proposed methods efficiently optimize and solve the optimal power flow problem with high efficiency and wide flexibility for implementation and analysis on different power system networks.

Major and expensive disturbances have a significant impact on supply chain management (SCM) and jeopardise the sustainability of any business. As COVID-19 pandemic, administrators are required to have strategies and techniques for safeguarding supply chains (CSs) and avoiding chain failures at every stage to be competitive in the market. Sustainability through SCM is described as a general practice that is emerging from the integration of pertinent, contemporary regenerative approaches and strategies. Therefore, it is possible to comprehend the idea of sustainable supply chain management (SSCM) as a business strategy for increasing eco-efficiency and productivity by recycling, reproducing and repurposing techniques under the circular economy. In order for SSCM practices to be successfully adopted in any organization, this study intends to evaluate the critical success factors (CSFs). Herein, nine CSFs were chosen following an exhaustive examination of the literature. The determined CSFs are analysed and evaluated through our constructed mathematical evaluator framework (MEF) based on Multi-Factors of Decision Making (MFoDM) methods, which are fortified with triangular neutrosophic sets (TriNSs) in obscurity and uncertainty situations. MEF conducts evaluation through several stages based on a set of MFoDM methods under the fortress of TriNSs. The best-worst method (BWM) is analysing nine CSFs, and the obtained weights assigned to nine CSFs represent the analysis's outcome. Posteriorly, measurement alternatives and ranking according to compromise solution (MARCOS) to prioritize and rank SCs as alternatives. Ultimately, we verified the validity of the constructed MEF through its application to reality throughout five SCs.

There has been increased activity in the study of methods for multidisciplinary analysis and design. This field of research has been a busy one over the past decade, driven by advances in computational methods and significant new developments in computer hardware. There is a concern, however, that while new computers will derive their computational speed through parallel processing, current algorithmic procedures that have roots in serial thinking are poor candidates for use on such machines - a paradigm shift is required! Among new advances in computational methods, soft computing techniques have enjoyed a remarkable period of development and growth. Of these, methods of neural computing, evolutionary search, and fuzzy logic have been the most extensively explored in problems of multidisciplinary analysis and design. The paper will summarize important accomplishments to-date, of neurocomputing, fuzzy-logic, and evolutionary search, including immune network modeling, in the field of multidisciplinary aerospace design.

Plant diseases are one of the factors that lead to yield and economic losses, which have a direct effect on national and international food production systems. One of the most essential ways to avoid agricultural product loss or reduction in amount is to diagnose plant diseases promptly and accurately. Hence, the diagnosis process for plants is crucial and should be conducted accurately. Moreover, this study focuses on this process by constructing an Artificiality Diagnostics Framework (ADF) to serve the study’s objectives which entailed conducting diagnosis for plants in a professional and precise manner over uncertain environments. Thus, neutrosophic theory is considered the principal ingredient in our ADF. Due to the ability of neutrosophic to divide images into Truth (T ( , Falsity(F), and Indeterminacy (I). Also, deep learning (DL) is considered another principal ingredient in treating vast samples of datasets. Our comparative analysis of the leaves of potatoes is conducted whether leveraging neutrosophic and without utilizing Neutrosophic. ResNet50, ResNet152, and Mobile Net are the principal ingredients for the training dataset. The findings of implementing these networks indicated that ResNet50 achieved the highest accuracy of 0.915 in the T domain, ResNet152 achieved the highest accuracy of 0.905 in the True(T) domain, and Mobile Net achieved the highest accuracy of 0.915 in Truth(T) domain. Accuracy of 0.863 in Indeterminate(I).

This paper presents an artificial intelligence monitoring system developed by the NASA Glenn Principal Investigator Microgravity Services project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment in time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a graphical display, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, platform structural modes, etc., and decide whether or not to run their experiments based on the acceleration environment associated with a specific event. This monitoring system is focused primarily on detecting the vibratory disturbance sources, but could be used as well to detect some of the transient disturbance sources, depending on the events duration. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

KTU S8 Soft Computing Notes (three modules only)

Soft computing is considered as one of the emerging areas of research in all fields of engineering and sciences. Soft computing algorithms have gained much popularity to solve engineering applications in recent years. These algorithms have gained the attention of researchers in solving problems in robotics, which is becoming inescapable in daily life and attracting extensive applications. The advantages of robots can only be harnessed with their mass introduction in the real world. In this paper, an introduction to soft computing techniques and their application in robotics is provided. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Robotics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49294.pdf Paper URL: https://www.ijtsrd.com/engineering/other/49294/soft-computing-in-robotics/matthew-n-o-sadiku

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

This issue introduces the state-of-art in granular soft computing for pattern recognition and mining and presents some novel contributions. Granulation is a computing paradigm, among others such as self-reproduction, self-organization, functioning of brain, Darwinian evolution, group behavior, cell membranes, and morphogenesis that are abstracted from natural phenomena. Granulation is inherent in human thinking and reasoning processes.

This issue introduces the state-of-art in granular soft computing for pattern recognition and mining and presents some novel contributions. Granulation is a computing paradigm, among others such as self-reproduction, self-organization, functioning of brain, Darwinian evolution, group behavior, cell membranes, and morphogenesis that are abstracted from natural phenomena. Granulation is inherent in human thinking and reasoning processes.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on predicting the compressive strength of SCC using Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include cement, limestone powder, fly ash, ground granulated blast furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, super-plasticizer, and viscosity-modifying admixtures. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results demonstrate that the AI-based model accurately predicts the compressive strength of self-compacting concrete.

The Vienna rectifier (VR) is a component that plays a pivotal role in various power electronics domains. It finds extensive utility in applications that require increased efficiency and minimized harmonic distortion. Some examples of these applications include solar photovoltaic grid-tied inverters, renewable energy systems, and electric motor variable speed drives. However, the ever-evolving system characteristics have spawned problems with the dependability and efficiency of such systems, making them less than ideal in both respects. This research proposes a ground-breaking method by using a three-phase VR model that is enhanced with a modified whale optimization algorithm (MWOA)-infused sliding mode controller (SMC), which is then, incorporated smoothly using the MATLAB/Simulink platform. The efficiency of the proposed system is shown by the construction of a working prototype, which also contributes to this demonstration. The suggested paradigm's enhanced performance is proven numerically and qualitatively via detailed comparisons with present systems.

Book Source: Digital Library of India Item 2015.198943 dc.contributor.author: Srinivasulu, Sanaga dc.date.accessioned: 2015-07-08T13:01:37Z dc.date.available: 2015-07-08T13:01:37Z dc.date.digitalpublicationdate: 2005-08-27 dc.identifier.barcode: 5990010101363 dc.identifier.origpath: /rawdataupload/upload/0101/365 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/198943 dc.description.scannerno: 14 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 240 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Civil Engineering dc.title: Improved Methodologies For Rainfall Runoff Modeling Using Conceptual And Soft Computing Techniques And Exploration Of Physical Significance In Ann Models

The potential of fuzzy sets and neural networks, often referred to as soft computing, for aiding in all aspects of manufacturing of advanced materials like ceramics is addressed. In design and manufacturing of advanced materials, it is desirable to find which of the many processing variables contribute most to the desired properties of the material. There is also interest in real time quality control of parameters that govern material properties during processing stages. The concepts of fuzzy sets and neural networks are briefly introduced and it is shown how they can be used in the design and manufacturing processes. These two computational methods are alternatives to other methods such as the Taguchi method. The two methods are demonstrated by using data collected at NASA Lewis Research Center. Future research directions are also discussed.

Crack detection and impact source identification in materials is a renowned problem found in variety of commercial and military applications like beams, bridges, turbines, pavements, armor plates, vehicle body plates, bones, teeth, and so on. This long-standing interest in development of CDISI is evident from variety of methods proposed in the literature. Ultrasonic guided waves are used for the crack detection. The crack detection is done by measuring lamb wave signals using the dual PZT transducer. Wireless inductively-coupled transducers are used for the crack detection. The wave velocities of concrete are measured by the portable transient elastic wave system to track the health of concrete. Automation for different crack detection and impact source identification methods is lately carried out in the literature using soft computing and VLSI techniques.

This paper presents the development of a Decision Support System for managing the labor based on the Soft Computing technique of Fuzzy Cognitive Maps. During labor, obstetricians manage and take decisions on the fetus delivery, and the procedure of the labor, monitoring continuously a visual recording of the fetus heart signal and entering contractions and they also take under consideration other physiological medical measurements and factors. Obstetricians utilize their experience and they decide either to proceed to a natural labor or to a Caesarean section. The developing system for managing labor delivery, will combine evaluation of cardiotocographic signal with other physiological data in order to create an advanced Decision Support System.

Constitutive models for concrete based on the microplane concept have repeatedly proven their ability to well-reproduce its non-linear response on material as well as structural scales. The major obstacle to a routine application of this class of models is, however, the calibration of microplane-related constants from macroscopic data. The goal of this paper is two-fold: (i) to introduce the basic ingredients of a robust inverse procedure for the determination of dominant parameters of the M4 model proposed by Bazant and co-workers based on cascade Artificial Neural Networks trained by Evolutionary Algorithm and (ii) to validate the proposed methodology against a representative set of experimental data. The obtained results demonstrate that the soft computing-based method is capable of delivering the searched response with an accuracy comparable to the values obtained by expert users.

Areas Covered In the Proceedings Includes: (1) "Invited Talks"; (2) "Rough Computing" Foundations and Applications"; (3) "Rough Set Theory and Its Application"; (4) "Fuzzy Set Theory and Its Applications"; (5) "Non-classical Logic and Approximate Reasoning"; (6) Information Granulation and Granular Computing"; (7) "Data Mining and Knowledge Discovery"; (8) "Machine Learning"; (9) Intelligent Agents and Systems".

Areas Covered In the Proceedings Includes: (1) "Invited Talks"; (2) "Rough Computing" Foundations and Applications"; (3) "Rough Set Theory and Its Application"; (4) "Fuzzy Set Theory and Its Applications"; (5) "Non-classical Logic and Approximate Reasoning"; (6) Information Granulation and Granular Computing"; (7) "Data Mining and Knowledge Discovery"; (8) "Machine Learning"; (9) Intelligent Agents and Systems".

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

With the proliferation of numerous soft computing (SC)–based maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, determining which algorithm performs better than others is becoming increasingly difficult. This is primarily due to the absence of standardized methods to benchmark their performances using consistent and systematic procedures. Moreover, the module technology, power ratings, and environmental conditions reported by numerous publications all differ. Based on these concerns, this paper presents a critical evaluation of the five most important and recent SC-based MPPTs, namely, genetic algorithm (GA), cuckoo search (CS), particle swarm optimization (PSO), differential evolution (DE), and evolutionary programming (EP). To perform a fair comparison, the initialization, selection, and stopping criteria for all methods are fixed in similar conditions. Thus, the performance is determined by its respective reproduction process. Simulation tests are performed using the MATLAB/SIMULINK environment. The performance of each algorithm is compared and evaluated based on its speed of convergence, accuracy, complexity, and success rate. The results indicate that EP appears to be the most promising and encouraging SC algorithm to be used in MPPT for a PV system under the multimodal partial shading condition.

With the proliferation of numerous soft computing (SC)–based maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, determining which algorithm performs better than others is becoming increasingly difficult. This is primarily due to the absence of standardized methods to benchmark their performances using consistent and systematic procedures. Moreover, the module technology, power ratings, and environmental conditions reported by numerous publications all differ. Based on these concerns, this paper presents a critical evaluation of the five most important and recent SC-based MPPTs, namely, genetic algorithm (GA), cuckoo search (CS), particle swarm optimization (PSO), differential evolution (DE), and evolutionary programming (EP). To perform a fair comparison, the initialization, selection, and stopping criteria for all methods are fixed in similar conditions. Thus, the performance is determined by its respective reproduction process. Simulation tests are performed using the MATLAB/SIMULINK environment. The performance of each algorithm is compared and evaluated based on its speed of convergence, accuracy, complexity, and success rate. The results indicate that EP appears to be the most promising and encouraging SC algorithm to be used in MPPT for a PV system under the multimodal partial shading condition.