计算智能：从概念到实现（英文版）
chapter one Foundations
Definitions
Biological Basis for Neural Networks
Neurons
Biological versus Artificial Neural Networks
Biological Basis for Evolutionary Computation
Chromosomes
Biological versus Artificial Chromo…
查看完整
Definitions
Biological Basis for Neural Networks
Neurons
Biological versus Artificial Neural Networks
Biological Basis for Evolutionary Computation
Chromosomes
Biological versus Artificial Chromo…
查看完整
Russell C.Eberhart，普度大学电子与计算机工程系主任，IEEE会士。与James Kennedy共同提出了粒子群优化算法。曾任IEEE神经网络委员会的主席。除了本书之外。他还著有《群体智能》（影印版由人民邮电出版社出版）等。
Yuhui Shi（史玉回），国际计算智能领域专家，现任Journal of Swarm Intelligence编委，IEEE CIS群体智能任务组主席，西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位，先后在美国、韩国、澳大利亚等地从事研究工作，曾任美国电子资讯系统公司专家长达9年。他还是《群体智能》一书的作者之一。
Yuhui Shi（史玉回），国际计算智能领域专家，现任Journal of Swarm Intelligence编委，IEEE CIS群体智能任务组主席，西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位，先后在美国、韩国、澳大利亚等地从事研究工作，曾任美国电子资讯系统公司专家长达9年。他还是《群体智能》一书的作者之一。
《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。
《计算智能：从概念到实现(英文版)》可作为高等院校相关专业高年级本科生或研究生的教材及参考用书，也可供从事智能科学、自动控制、系统科学、计算机科学、应用数学等领域研究的教师和科研人员参考。
《计算智能：从概念到实现(英文版)》可作为高等院校相关专业高年级本科生或研究生的教材及参考用书，也可供从事智能科学、自动控制、系统科学、计算机科学、应用数学等领域研究的教师和科研人员参考。
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
^ 收 起
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
^ 收 起
Russell C.Eberhart，普度大学电子与计算机工程系主任，IEEE会士。与James Kennedy共同提出了粒子群优化算法。曾任IEEE神经网络委员会的主席。除了本书之外。他还著有《群体智能》（影印版由人民邮电出版社出版）等。
Yuhui Shi（史玉回），国际计算智能领域专家，现任Journal of Swarm Intelligence编委，IEEE CIS群体智能任务组主席，西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位，先后在美国、韩国、澳大利亚等地从事研究工作，曾任美国电子资讯系统公司专家长达9年。他还是《群体智能》一书的作者之一。
Yuhui Shi（史玉回），国际计算智能领域专家，现任Journal of Swarm Intelligence编委，IEEE CIS群体智能任务组主席，西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位，先后在美国、韩国、澳大利亚等地从事研究工作，曾任美国电子资讯系统公司专家长达9年。他还是《群体智能》一书的作者之一。
《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。
《计算智能：从概念到实现(英文版)》可作为高等院校相关专业高年级本科生或研究生的教材及参考用书，也可供从事智能科学、自动控制、系统科学、计算机科学、应用数学等领域研究的教师和科研人员参考。
《计算智能：从概念到实现(英文版)》可作为高等院校相关专业高年级本科生或研究生的教材及参考用书，也可供从事智能科学、自动控制、系统科学、计算机科学、应用数学等领域研究的教师和科研人员参考。
比价列表
