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Inductive Learning Algorithms for Complex Systems Modeling

"Inductive Learning Algorithms for Complex Systems Modeling" is published by Taylor & Francis Group in 2019 - Milton, it has 380 pages and the language of the book is English.


“Inductive Learning Algorithms for Complex Systems Modeling” Metadata:

  • Title: ➤  Inductive Learning Algorithms for Complex Systems Modeling
  • Author:
  • Language: English
  • Number of Pages: 380
  • Publisher: Taylor & Francis Group
  • Publish Date:
  • Publish Location: Milton

“Inductive Learning Algorithms for Complex Systems Modeling” Subjects and Themes:

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"Inductive Learning Algorithms for Complex Systems Modeling" Description:

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Cover Page -- Title Page -- Copyright Page -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction -- 1 Systems and Cybernetics -- 1.1 Definitions -- 1.2 Model and simulation -- 1.3 Concept of black box -- 2 Self-Organization Modeling -- 2.1 Neural approach -- 2.2 Inductive approach -- 3 Inductive Learning Methods -- 3.1 Principal shortcoming in model development -- 3.2 Principle of self-organization -- 3.3 Basic technique -- 3.4 Selection criteria or objective functions -- 3.5 Heuristics used in problem-solving -- Chapter 2: Inductive Learning Algorithms -- 1 Self-Organization Method -- 1.1 Basic iterative algorithm -- 2 Network Structures -- 2.1 Multilayer algorithm -- 2.2 Combinatorial algorithm -- 2.3 Recursive scheme for faster combinatorial sorting -- 2.4 Multilayered structures using combinatorial setup -- 2.5 Selectional-combinatorial multilayer algorithm -- 2.6 Multilayer algorithm with propagating residuals (front propagation algorithm) -- 2.7 Harmonic Algorithm -- 2.8 New algorithms -- 3 Long-Term Quantitative Predictions -- 3.1 Autocorrelation functions -- 3.2 Correlation interval as a measure of predictability -- 3.3 Principal characteristics for predictions -- 4 Dialogue Language Generalization -- 4.1 Regular (subjective) system analysis -- 4.2 Multilevel (objective) analysis -- 4.3 Multilevel algorithm -- Chapter 3: Noise Immunity and Convergence -- 1 Analogy with Information Theory -- 1.1 Basic concepts of information and self-organization theories -- 1.2 Shannon's second theorem -- 1.3 Law of conservation of redundancy -- 1.4 Model complexity versus transmission band -- 2 Classification and Analysis of Criteria -- 2.1 Accuracy criteria -- 2.2 Consistent criteria -- 2.3 Combined criteria -- 2.4 Correlational criteria -- 2.5 Relationships among the criteria -- 3 Improvement of Noise Immunity

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