Fundamentals of Computational Intelligence - Info and Reading Options

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Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation -- Table of Contents -- Acknowledgments -- Chapter 1: Introduction to Computational Intelligence -- 1.1 Welcome to Computational Intelligence -- 1.2 What Makes This Book Special -- 1.3 What This Book Covers -- 1.4 How to Use This Book -- 1.5 Final Thoughts Before You Get Started -- Part I: Neural Networks -- Chapter 2: Introduction and Single-Layer Neural Networks -- 2.1 Short History of Neural Networks -- 2.2 Rosenblatt's Neuron -- 2.3 Perceptron Training Algorithm -- 2.3.1 Test Problem -- 2.3.2 Constructing Learning Rules -- 2.3.3 Unified Learning Rule -- 2.3.4 Training Multiple-Neuron Perceptrons -- 2.3.4.1 Problem Statement -- 2.4 The Perceptron Convergence Theorem -- 2.5 Computer Experiment Using Perceptrons -- 2.6 Activation Functions -- 2.6.1 Threshold Function -- 2.6.2 Sigmoid Function -- Exercises -- Chapter 3: Multilayer Neural Networks and Backpropagation -- 3.1 Universal Approximation Theory -- 3.2 The Backpropagation Training Algorithm -- 3.2.1 The Description of the Algorithm -- 3.2.2 The Strategy for Improving the Algorithm -- 3.2.3 The Design Procedure of the Algorithm -- 3.3 Batch Learning and Online Learning -- 3.3.1 Batch Learning -- 3.3.2 Online Learning -- 3.4 Cross-Validation and Generalization -- 3.4.1 Cross-Validation -- 3.4.2 Generalization -- 3.4.3 Convolutional Neural Networks -- 3.5 Computer Experiment Using Backpropagation -- Exercises -- Chapter 4: Radial-Basis Function Networks -- 4.1 Radial-Basis Functions -- 4.2 The Interpolation Problem -- 4.3 Training Algorithms for Radial-Basis Function Networks -- 4.3.1 Layered Structure of a Radial-Basis Function Network -- 4.3.2 Modification of the Structure of RBF Network -- 4.3.3 Hybrid Learning Process -- 4.4 Universal Approximation -- 4.5 Kernel Regression

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