《計算智慧型：從概念到實現》是由人民郵電出版社出版的圖書，全書面向智慧型系統學科的前沿領域，系統地討論了計算智慧型的理論、技術及其套用，比較全面地反映了計算智慧型研究和套用的最新進展。書中涵蓋了模糊控制、神經網路控制、進化計算以及其他一些技術及套用的內容。全書提供了大量的實用案例，重點強調實際的套用和計算工具，這些對於計算智慧型領域的進一步發展是非常有意義的。《計算智慧型:從概念到實現(英文版)》取材新穎，內容深入淺出，材料豐富，理論密切結合實際，具有較高的學術水平和參考價值。全書可作為高等院校相關專業高年級本科生或研究生的教材及參考用書，也可供從事智慧型科學、自動控制、系統科學、計算機科學、套用數學等領域研究的教師和科研人員參考。
基本介紹
- 書名：計算智慧型：從概念到實現
- ISBN：9787115194039
- 頁數：467頁
- 出版社：人民郵電出版社
- 開本：16
圖書信息,作者簡介,媒體評論,目錄,
圖書信息
出版社: 人民郵電出版社; 第1版 (2009年2月1日)
外文書名: Computational Intelligence: Concepts to Implementations, First Edition
叢書名: 圖靈原版計算機科學系列
平裝: 467頁
正文語種: 英語
開本: 16
ISBN: 9787115194039
條形碼: 9787115194039
尺寸: 23.2 x 18.4 x 2.2 cm
重量: 680 g
作者簡介
作者：(美國)埃伯哈特 (Russell C.Eberhart) (美國)史玉回 (Yuhui Shi)
Russell C.Eberhart，普度大學電子與計算機工程系主任，IEEE會士。與James Kennedy共同提出了粒子群最佳化算法。曾任IEEE神經網路委員會的主席。除了本書之外。他還著有《群體智慧型》（影印版由人民郵電出版社出版）等。
Yuhui Shi（史玉回），國際計算智慧型領域專家，現任Journal of Swarm Intelligence編委，IEEE CIS群體智慧型任務組主席，西交利物浦大學電子與電氣工程系教授。1992年獲東南大學博士學位，先後在美國、韓國、澳大利亞等地從事研究工作，曾任美國電子資訊系統公司專家長達9年。他還是《群體智慧型》一書的作者之一。
媒體評論
“這是第一部如此全面的計算智慧型教科書，包括了大量的實踐示例。”
——Shun-ichi Amari，日本理化研究所腦科學研究機構
“本書強調計算智慧型的基礎是演化計算，這種全新的視角使其獨樹一幟。本書有非常豐富的實際套用和計算工具，對於計算智慧型領域的發展意義重大。”
——Xin Yao，伯明罕計算智慧型與套用研究中心
目錄
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
