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
PART I NEURAL NETWORKS
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
Exercises
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
Exercises
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
Exercises
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
Exercises
PART II FUZZY SET THEORY AND FUZZY LOGIC
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
Exercises
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
Exercises
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
Exercises
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
Exercises
PART III EVOLUTIONARY COMPUTATION
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
Exercises
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
Exercises
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
Exercises
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
Exercises
References
