New Textbook on Computational Intelligence

The SMC Society is delighted to announce the publishing of the book:

Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation

by James M. Keller, Derong Liu, David B. Fogel
ISBN: 978-1-119-21434-2
July 2016, Wiley-IEEE Press
Hardcover, 378 pages, $120.00
Purchase at Wiley

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.

The textbook:

  • discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks;
  • covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals;
  • includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems;

Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Talbe of Contents

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

2. Introduction and Single-Layer Neural Networks
2.1 Short History of Neural Networks
2.2 Rosenblatt’s Neuron
2.3 Perceptron Training Algorithm
2.4 The Perceptron Convergence Theorem
2.5 Computer Experiment Using Perceptrons
2.6 Activation Functions

3. Multilayer Neural Networks and Backpropagation

3.1 Universal Approximation Theory

3.2 The Backpropagation Training Algorithm

3.3 Batch Learning and Online Learning

3.4 Cross-Validation and Generalization

3.5 Computer Experiment Using Backpropagation


4. Radial-Basis Function Networks

4.1 Radial-Basis Functions

4.2 The Interpolation Problem

4.3 Training Algorithms For Radial-Basis Function Networks

4.4 Universal Approximation

4.5 Kernel Regression


5. Recurrent Neural Networks

5.1 The Hopfield Network

5.2 The Grossberg Network

5.3 Cellular Neural Networks

5.4 Neurodynamics and Optimization

5.5 Stability Analysis of Recurrent Neural Networks



6. Basic Fuzzy Set Theory

6.1 Introduction

6.2 A Brief History

6.3 Fuzzy Membership Functions and Operators

6.4 Alpha-Cuts, The Decomposition Theorem, and The Extension Principle

6.5 Compensatory Operators

6.6 Conclusions


7. Fuzzy Relations and Fuzzy Logic Inference

7.1 Introduction

7.2 Fuzzy Relations and Propositions

7.3 Fuzzy Logic Inference

7.4 Fuzzy Logic For Real-Valued Inputs

7.5 Where Do The Rules Come From?

7.6 Chapter Summary


8. Fuzzy Clustering and Classification

8.1 Introduction to Fuzzy Clustering

8.2 Fuzzy c-Means

8.3 An Extension of The Fuzzy c-Means

8.4 Possibilistic c-Means

8.5 Fuzzy Classifiers: Fuzzy k-Nearest Neighbors

8.6 Chapter Summary


9. Fuzzy Measures and Fuzzy Integrals

9.1 Fuzzy Measures

9.2 Fuzzy Integrals

9.3 Training The Fuzzy Integrals

9.4 Summary and Final Thoughts



10. Evolutionary Computation

10.1 Basic Ideas and Fundamentals

10.2 Evolutionary Algorithms: Generate and Test

10.3 Representation, Search, and Selection Operators

10.4 Major Research and Application Areas

10.5 Summary


11. Evolutionary Optimization

11.1 Global Numerical Optimization

11.2 Combinatorial Optimization

11.3 Some Mathematical Considerations

11.4 Constraint Handling

11.5 Self-Adaptation

11.6 Summary


12. Evolutionary Learning and Problem Solving

12.1 Evolving Parameters of A Regression Equation

12.2 Evolving The Structure and Parameters of Input–Output Systems

12.3 Evolving Clusters

12.4 Evolutionary Classification Models

12.5 Evolutionary Control Systems

12.6 Evolutionary Games

12.7 Summary


13. Collective Intelligence and Other Extensions of Evolutionary Computation

13.1 Particle Swarm Optimization

13.2 Differential Evolution

13.3 Ant Colony Optimization

13.4 Evolvable Hardware

13.5 Interactive Evolutionary Computation

13.6 Multicriteria Evolutionary Optimization

13.7 Summary