计算智能：从概念到实现

chapter one Foundations

Definitions

Biological Basis for Neural Networks

Neurons

Biological versus Artificial Neural Networks

Biological Basis for Evolutionary Computation

Chromosomes

Biological versus Artificial Chromosomes

Behavioral Motivations for Fuzzy Logic

Myths about Computational Intelligence

Computational Intelligence Application Areas

Neural Networks

Evolutionary Computation

Fuzzy Logic

Summary

Exercises

chapter two Computational Intelligence

Adaptation

Adaptation versus Learning

Three Types of Adaptation

Three Spaces of Adaptation

Self-organization and Evolution

Evolution beyond Darwin

Historical Views of Computational Intelligence

Computational Intelligence as Adaptation and Self-organization

The Ability to Generalize

Computational Intelligence and Soft Computing versus Artificial Intelligence and Hard Computing

Summary

Exercises

chapter three Evolutionary Computation Concepts and Paradigms

History of Evolutionary Computation

Genetic Algorithms

Evolutionary Programming

Evolution Strategies

Genetic Programming

Particle Swarm Optimization

Toward Unification

Evolutionary Computation Overview

EC Paradigm Attributes

Implementation

Genetic Algorithms

Overview of Genetic Algorithms

A Sample GA Problem

Review of GA Operations in the Simple Example

Schemata and the Schema Theorem

Comments on Genetic Algorithms

Evolutionary Programming

Evolutionary Programming Procedure

Finite State Machine Evolution for Prediction

Function Optimization

Comments on Evolutionary Programming

Evolution Strategies

Selection

Key Issues in Evolution Strategies

Genetic Programming

Particle Swarm Optimization

Developments

Resources

Summary

Exercises

chapter four Evolutionary Computation Implementations

Implementation Issues

Homogeneous versus Heterogeneous Representation

Population Adaptation versus Individual Adaptation

Static versus Dynamic Adaptation

Flowcharts versus Finite State Machines

Handling Multiple Similar Cases

Allocating and Freeing Memory Space

Error Checking

Genetic Algorithm Implementation

Programming Genetic Algorithms

Running the GA Implementation

Particle Swarm Optimization Implementation

Programming the PSO Implementation

Programming the Co-evolutionary PSO

Running the PSO Implementation

Summary

Exercises

chapter five Neural Network Concepts and Paradigms

Neural Network History

Where Did Neural Networks Get Their Name?

The Age of Camelot

The Dark Age

The Renaissance

The Age of Neoconnectionism

The Age of Computational Intelligence

What Neural Networks Are andWhy They Are Useful

Neural Network Components and Terminology

Terminology

Input and Output Patterns

NetworkWeights

Processing Elements

Processing Element Activation Functions

Neural Network Topologies

Terminology

Two-layer Networks

Multilayer Networks

Neural Network Adaptation

Terminology

Hebbian Adaptation

Competitive Adaptation

Multilayer Error Correction Adaptation

Summary of Adaptation Procedures

ComparingNeuralNetworks and Other Information ProcessingMethods

Stochastic Approximation

Kalman Filters

Linear and Nonlinear Regression

Correlation

Bayes Classification

Vector Quantization

Radial Basis Functions

Computational Intelligence

Preprocessing

Selecting Training， Test， and Validation Datasets

Preparing Data

Postprocessing

Denormalization of Output Data

Summary

Exercises

chapter six Neural Network Implementations

Implementation Issues

Topology

Back-propagation Network Initialization and Normalization

LearningVector QuantizerNetwork Initialization andNormalization

Feedforward Calculations for the Back-propagation Network

Feedforward Calculations for the LVQ-I Net

Back-propagation SupervisedAdaptation by Error Back-propagation

LVQ Unsupervised Adaptation Calculations

The LVQ Supervised Adaptation Algorithm

Issues in Evolving Neural Networks

Advantages and Disadvantages of Previous EvolutionaryApproaches

Evolving Neural Networks with Particle Swarm Optimization

Back-propagation Implementation

Programming a Back-propagation Neural Network

Running the Back-propagation Implementation

The Kohonen Network Implementations

Programming the Learning Vector Quantizer

Running the LVQ Implementation

Programming the Self-organizing Feature Map

Running the SOFM Implementation

Evolutionary Back-propagation Network Implementation

Programming the Evolutionary Back-propagation Network

Running the Evolutionary Back-propagation Network

Summary

Exercises

chapter seven Fuzzy Systems Concepts and Paradigms

History

Fuzzy Sets and Fuzzy Logic

Logic， Fuzzy and Otherwise

Fuzziness Is Not Probability

The Theory of Fuzzy Sets

Fuzzy Set Membership Functions

Linguistic Variables

Linguistic Hedges

Approximate Reasoning

Paradoxes in Fuzzy Logic

Equality of Fuzzy Sets

Containment

NOT： The Complement of a Fuzzy Set

AND： The Intersection of Fuzzy Sets

OR： The Union of Fuzzy Sets

Compensatory Operators

Fuzzy Rules

Fuzzification

Fuzzy Rules Fire in Parallel

Defuzzification

Other Defuzzification Methods

Measures of Fuzziness

Developing a Fuzzy Controller

Why Fuzzy Control

A Fuzzy Controller

Building a Mamdani-type Fuzzy Controller

Fuzzy Controller Operation

Takagi-Sugeno-Kang Method

Summary

Exercises

chapter eight Fuzzy Systems Implementations

Implementation Issues

Fuzzy Rule Representation

Evolutionary Design of a Fuzzy Rule System

An Object-oriented Language： C++

Fuzzy Rule System Implementation

Programming Fuzzy Rule Systems

Running the Fuzzy Rule System

Iris Dataset Application

Evolving Fuzzy Rule Systems

Programming the Evolutionary Fuzzy Rule System

Running the Evolutionary Fuzzy Rule System

Summary

Exercises

chapter nine Computational Intelligence Implementations

Implementation Issues

Adaptation of Genetic Algorithms

Fuzzy Adaptation

Knowledge Elicitation

Fuzzy Evolutionary Fuzzy Rule System Implementation

Programming the Fuzzy Evolutionary Fuzzy Rule System

Running the Fuzzy Evolutionary Fuzzy Rule System

Choosing the Best Tools

Strengths andWeaknesses

Modeling and Optimization

Practical Issues

Applying Computational Intelligence to Data Mining

An Example Data Mining System

Summary

Exercises

chapter ten Performance Metrics

General Issues

Selecting Gold Standards

Partitioning the Patterns for Training， Testing， and Validation

Cross Validation

Fitness and Fitness Functions

Parametric and Nonparametric Statistics

Percent Correct

Average Sum-squared Error

Absolute Error

Normalized Error

Evolutionary Algorithm Effectiveness Metrics

Mann-Whitney U Test

Receiver Operating Characteristic Curves

Recall and Precision

Other ROC-related Measures

Confusion Matrices

Chi-square Test

Summary

Exercises

chapter eleven Analysis and Explanation

Sensitivity Analysis

Relation Factors

Zurada Sensitivity Analysis

Evolutionary Computation Sensitivity Analysis

Hinton Diagrams

Computational Intelligence Tools for Explanation Facilities

Explanation Facility Requirements

Neural Network Explanation

Fuzzy Expert System Explanation

Evolutionary Computation Tools for Explanation

An Example Neural Network Explanation Facility

Summary

Exercises

Bibliography

Index

About the Authors

《计算智能：从概念到实现(英文版)》面向智能系统学科的前沿领域，系统地讨论了计算智能的理论、技术及其应用，比较全面地反映了计算智能研究和应用的最新进展。书中涵盖了模糊控制、神经网络控制、进化计算以及其他一些技术及应用的内容。《计算智能：从概念到实现(英文版)》提供了大量的实用案例，重点强调实际的应用和计算工具，这些对于计算智能领域的进一步发展是非常有意义的。《计算智能：从概念到实现(英文版)》取材新颖，内容深入浅出，材料丰富，理论密切结合实际，具有较高的学术水平和参考价值。
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