《計(jì)算智能：從概念到實(shí)現(xiàn)(英文版)》面向智能系統(tǒng)學(xué)科的前沿領(lǐng)域，系統(tǒng)地討論了計(jì)算智能的理論、技術(shù)及其應(yīng)用，比較全面地反映了計(jì)算智能研究和應(yīng)用的最新進(jìn)展。書中涵蓋了模糊控制、神經(jīng)網(wǎng)絡(luò)控制、進(jìn)化計(jì)算以及其他一些技術(shù)及應(yīng)用的內(nèi)容。《計(jì)算智能：從概念到實(shí)現(xiàn)(英文版)》提供了大量的實(shí)用案例，重點(diǎn)強(qiáng)調(diào)實(shí)際的應(yīng)用和計(jì)算工具，這些對于計(jì)算智能領(lǐng)域的進(jìn)一步發(fā)展是非常有意義的。《計(jì)算智能：從概念到實(shí)現(xiàn)(英文版)》取材新穎，內(nèi)容深入淺出，材料豐富，理論密切結(jié)合實(shí)際，具有較高的學(xué)術(shù)水平和參考價(jià)值。
《計(jì)算智能：從概念到實(shí)現(xiàn)(英文版)》可作為高等院校相關(guān)專業(yè)高年級本科生或研究生的教材及參考用書，也可供從事智能科學(xué)、自動控制、系統(tǒng)科學(xué)、計(jì)算機(jī)科學(xué)、應(yīng)用數(shù)學(xué)等領(lǐng)域研究的教師和科研人員參考。
Russell C.Eberhart，普度大學(xué)電子與計(jì)算機(jī)工程系主任，IEEE會士。與James Kennedy共同提出了粒子群優(yōu)化算法。曾任IEEE神經(jīng)網(wǎng)絡(luò)委員會的主席。除了本書之外。他還著有《群體智能》（*版由人民郵電出版社出版）等。
Yuhui Shi（史玉回），國際計(jì)算智能領(lǐng)域?qū)＜遥F(xiàn)任Journal of Swarm Intelligence編委，IEEE CIS群體智能任務(wù)組主席，西交利物浦大學(xué)電子與電氣工程系教授。1992年獲東南大學(xué)博士學(xué)位，先后在美國、韓國、澳大利亞等地從事研究工作，曾任美國電子資訊系統(tǒng)公司專家長達(dá)9年。他還是《群體智能》一書的作者之一。
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 Quanti
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