| 词条 | 计算智能：从概念到实现 |
| 释义 | 图书信息出版社: 人民邮电出版社; 第1版 (2009年2月1日) 外文书名: Computational Intelligence: Concepts to Implementations, First Edition 丛书名: 图灵原版计算机科学系列 平装: 467页 正文语种: 英语 开本: 16 ISBN: 9787115194039 条形码: 9787115194039 尺寸: 23.2 x 18.4 x 2.2 cm 重量: 680 g 作者简介作者：(美国)埃伯哈特 (Russell C.Eberhart) (美国)史玉回 (Yuhui Shi) 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。 内容简介《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。 媒体评论“这是第一部如此全面的计算智能教科书，包括了大量的实践示例。” ——Shun-ichi Amari，日本理化研究所脑科学研究机构 “本书强调计算智能的基础是演化计算，这种全新的视角使其独树一帜。本书有非常丰富的实际应用和计算工具，对于计算智能领域的发展意义重大。” ——Xin Yao，伯明翰计算智能与应用研究中心 目录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 |
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