计算智能:Computational Intelligence: Concepts to Implementations

计算智能
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作者: (Russell C.Eberhart) (Yuhui Shi)
出版社: 人民邮电出版社
2009-02
版次: 1
ISBN: 9787115194039
定价: 69.00
装帧: 平装
开本: 16开
纸张: 胶版纸
页数: 467页
字数: 586千字
正文语种: 英语
  • 《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。 RussellC.Eberhart,普度大学电子与计算机工程系主任,IEEE会士。与JamesKennedy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。
    YuhuiShi(史玉回),国际计算智能领域专家,现任JournalofSwarmIntelligence编委,IEEECIS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。 chapteroneFoundations
    Definitions
    BiologicalBasisforNeuralNetworks
    Neurons
    BiologicalversusArtificialNeuralNetworks
    BiologicalBasisforEvolutionaryComputation
    Chromosomes
    BiologicalversusArtificialChromosomes
    BehavioralMotivationsforFuzzyLogic
    MythsaboutComputationalIntelligence
    ComputationalIntelligenceApplicationAreas
    NeuralNetworks
    EvolutionaryComputation
    FuzzyLogic
    Summary
    Exercises

    chaptertwoComputationalIntelligence
    Adaptation
    AdaptationversusLearning
    ThreeTypesofAdaptation
    ThreeSpacesofAdaptation
    Self-organizationandEvolution
    EvolutionbeyondDarwin
    HistoricalViewsofComputationalIntelligence
    ComputationalIntelligenceasAdaptationandSelf-organization
    TheAbilitytoGeneralize
    ComputationalIntelligenceandSoftComputingversusArtificialIntelligenceandHardComputing
    Summary
    Exercises

    chapterthreeEvolutionaryComputationConceptsandParadigms
    HistoryofEvolutionaryComputation
    GeneticAlgorithms
    EvolutionaryProgramming
    EvolutionStrategies
    GeneticProgramming
    ParticleSwarmOptimization
    TowardUnification
    EvolutionaryComputationOverview
    ECParadigmAttributes
    Implementation
    GeneticAlgorithms
    OverviewofGeneticAlgorithms
    ASampleGAProblem
    ReviewofGAOperationsintheSimpleExample
    SchemataandtheSchemaTheorem
    CommentsonGeneticAlgorithms
    EvolutionaryProgramming
    EvolutionaryProgrammingProcedure
    FiniteStateMachineEvolutionforPrediction
    FunctionOptimization
    CommentsonEvolutionaryProgramming
    EvolutionStrategies
    Selection
    KeyIssuesinEvolutionStrategies
    GeneticProgramming
    ParticleSwarmOptimization
    Developments
    Resources
    Summary
    Exercises

    chapterfourEvolutionaryComputationImplementations
    ImplementationIssues
    HomogeneousversusHeterogeneousRepresentation
    PopulationAdaptationversusIndividualAdaptation
    StaticversusDynamicAdaptation
    FlowchartsversusFiniteStateMachines
    HandlingMultipleSimilarCases
    AllocatingandFreeingMemorySpace
    ErrorChecking
    GeneticAlgorithmImplementation
    ProgrammingGeneticAlgorithms
    RunningtheGAImplementation
    ParticleSwarmOptimizationImplementation
    ProgrammingthePSOImplementation
    ProgrammingtheCo-evolutionaryPSO
    RunningthePSOImplementation
    Summary
    Exercises

    chapterfiveNeuralNetworkConceptsandParadigms
    NeuralNetworkHistory
    WhereDidNeuralNetworksGetTheirName?
    TheAgeofCamelot
    TheDarkAge
    TheRenaissance
    TheAgeofNeoconnectionism
    TheAgeofComputationalIntelligence
    WhatNeuralNetworksAreandWhyTheyAreUseful
    NeuralNetworkComponentsandTerminology
    Terminology
    InputandOutputPatterns
    NetworkWeights
    ProcessingElements
    ProcessingElementActivationFunctions
    NeuralNetworkTopologies
    Terminology
    Two-layerNetworks
    MultilayerNetworks
    NeuralNetworkAdaptation
    Terminology
    HebbianAdaptation
    CompetitiveAdaptation
    MultilayerErrorCorrectionAdaptation
    SummaryofAdaptationProcedures
    ComparingNeuralNetworksandOtherInformationProcessingMethods
    StochasticApproximation
    KalmanFilters
    LinearandNonlinearRegression
    Correlation
    BayesClassification
    VectorQuantization
    RadialBasisFunctions
    ComputationalIntelligence
    Preprocessing
    SelectingTraining,Test,andValidationDatasets
    PreparingData
    Postprocessing
    DenormalizationofOutputData
    Summary
    Exercises

    chaptersixNeuralNetworkImplementations
    ImplementationIssues
    Topology
    Back-propagationNetworkInitializationandNormalization
    LearningVectorQuantizerNetworkInitializationandNormalization
    FeedforwardCalculationsfortheBack-propagationNetwork
    FeedforwardCalculationsfortheLVQ-INet
    Back-propagationSupervisedAdaptationbyErrorBack-propagation
    LVQUnsupervisedAdaptationCalculations
    TheLVQSupervisedAdaptationAlgorithm
    IssuesinEvolvingNeuralNetworks
    AdvantagesandDisadvantagesofPreviousEvolutionaryApproaches
    EvolvingNeuralNetworkswithParticleSwarmOptimization
    Back-propagationImplementation
    ProgrammingaBack-propagationNeuralNetwork
    RunningtheBack-propagationImplementation
    TheKohonenNetworkImplementations
    ProgrammingtheLearningVectorQuantizer
    RunningtheLVQImplementation
    ProgrammingtheSelf-organizingFeatureMap
    RunningtheSOFMImplementation
    EvolutionaryBack-propagationNetworkImplementation
    ProgrammingtheEvolutionaryBack-propagationNetwork
    RunningtheEvolutionaryBack-propagationNetwork
    Summary
    Exercises

    chaptersevenFuzzySystemsConceptsandParadigms
    History
    FuzzySetsandFuzzyLogic
    Logic,FuzzyandOtherwise
    FuzzinessIsNotProbability
    TheTheoryofFuzzySets
    FuzzySetMembershipFunctions
    LinguisticVariables
    LinguisticHedges
    ApproximateReasoning
    ParadoxesinFuzzyLogic
    EqualityofFuzzySets
    Containment
    NOT:TheComplementofaFuzzySet
    AND:TheIntersectionofFuzzySets
    OR:TheUnionofFuzzySets
    CompensatoryOperators
    FuzzyRules
    Fuzzification
    FuzzyRulesFireinParallel
    Defuzzification
    OtherDefuzzificationMethods
    MeasuresofFuzziness
    DevelopingaFuzzyController
    WhyFuzzyControl
    AFuzzyController
    BuildingaMamdani-typeFuzzyController
    FuzzyControllerOperation
    Takagi-Sugeno-KangMethod
    Summary
    Exercises

    chaptereightFuzzySystemsImplementations
    ImplementationIssues
    FuzzyRuleRepresentation
    EvolutionaryDesignofaFuzzyRuleSystem
    AnObject-orientedLanguage:C++
    FuzzyRuleSystemImplementation
    ProgrammingFuzzyRuleSystems
    RunningtheFuzzyRuleSystem
    IrisDatasetApplication
    EvolvingFuzzyRuleSystems
    ProgrammingtheEvolutionaryFuzzyRuleSystem
    RunningtheEvolutionaryFuzzyRuleSystem
    Summary
    Exercises

    chapternineComputationalIntelligenceImplementations
    ImplementationIssues
    AdaptationofGeneticAlgorithms
    FuzzyAdaptation
    KnowledgeElicitation
    FuzzyEvolutionaryFuzzyRuleSystemImplementation
    ProgrammingtheFuzzyEvolutionaryFuzzyRuleSystem
    RunningtheFuzzyEvolutionaryFuzzyRuleSystem
    ChoosingtheBestTools
    StrengthsandWeaknesses
    ModelingandOptimization
    PracticalIssues
    ApplyingComputationalIntelligencetoDataMining
    AnExampleDataMiningSystem
    Summary
    Exercises

    chaptertenPerformanceMetrics
    GeneralIssues
    SelectingGoldStandards
    PartitioningthePatternsforTraining,Testing,andValidation
    CrossValidation
    FitnessandFitnessFunctions
    ParametricandNonparametricStatistics
    PercentCorrect
    AverageSum-squaredError
    AbsoluteError
    NormalizedError
    EvolutionaryAlgorithmEffectivenessMetrics
    Mann-WhitneyUTest
    ReceiverOperatingCharacteristicCurves
    RecallandPrecision
    OtherROC-relatedMeasures
    ConfusionMatrices
    Chi-squareTest
    Summary
    Exercises

    chapterelevenAnalysisandExplanation
    SensitivityAnalysis
    RelationFactors
    ZuradaSensitivityAnalysis
    EvolutionaryComputationSensitivityAnalysis
    HintonDiagrams
    ComputationalIntelligenceToolsforExplanationFacilities
    ExplanationFacilityRequirements
    NeuralNetworkExplanation
    FuzzyExpertSystemExplanation
    EvolutionaryComputationToolsforExplanation
    AnExampleNeuralNetworkExplanationFacility
    Summary
    Exercises
    Bibliography
    Index
    AbouttheAuthors
  • 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。
  • 作者简介:
    RussellC.Eberhart,普度大学电子与计算机工程系主任,IEEE会士。与JamesKennedy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。
    YuhuiShi(史玉回),国际计算智能领域专家,现任JournalofSwarmIntelligence编委,IEEECIS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。
  • 目录:
    chapteroneFoundations
    Definitions
    BiologicalBasisforNeuralNetworks
    Neurons
    BiologicalversusArtificialNeuralNetworks
    BiologicalBasisforEvolutionaryComputation
    Chromosomes
    BiologicalversusArtificialChromosomes
    BehavioralMotivationsforFuzzyLogic
    MythsaboutComputationalIntelligence
    ComputationalIntelligenceApplicationAreas
    NeuralNetworks
    EvolutionaryComputation
    FuzzyLogic
    Summary
    Exercises

    chaptertwoComputationalIntelligence
    Adaptation
    AdaptationversusLearning
    ThreeTypesofAdaptation
    ThreeSpacesofAdaptation
    Self-organizationandEvolution
    EvolutionbeyondDarwin
    HistoricalViewsofComputationalIntelligence
    ComputationalIntelligenceasAdaptationandSelf-organization
    TheAbilitytoGeneralize
    ComputationalIntelligenceandSoftComputingversusArtificialIntelligenceandHardComputing
    Summary
    Exercises

    chapterthreeEvolutionaryComputationConceptsandParadigms
    HistoryofEvolutionaryComputation
    GeneticAlgorithms
    EvolutionaryProgramming
    EvolutionStrategies
    GeneticProgramming
    ParticleSwarmOptimization
    TowardUnification
    EvolutionaryComputationOverview
    ECParadigmAttributes
    Implementation
    GeneticAlgorithms
    OverviewofGeneticAlgorithms
    ASampleGAProblem
    ReviewofGAOperationsintheSimpleExample
    SchemataandtheSchemaTheorem
    CommentsonGeneticAlgorithms
    EvolutionaryProgramming
    EvolutionaryProgrammingProcedure
    FiniteStateMachineEvolutionforPrediction
    FunctionOptimization
    CommentsonEvolutionaryProgramming
    EvolutionStrategies
    Selection
    KeyIssuesinEvolutionStrategies
    GeneticProgramming
    ParticleSwarmOptimization
    Developments
    Resources
    Summary
    Exercises

    chapterfourEvolutionaryComputationImplementations
    ImplementationIssues
    HomogeneousversusHeterogeneousRepresentation
    PopulationAdaptationversusIndividualAdaptation
    StaticversusDynamicAdaptation
    FlowchartsversusFiniteStateMachines
    HandlingMultipleSimilarCases
    AllocatingandFreeingMemorySpace
    ErrorChecking
    GeneticAlgorithmImplementation
    ProgrammingGeneticAlgorithms
    RunningtheGAImplementation
    ParticleSwarmOptimizationImplementation
    ProgrammingthePSOImplementation
    ProgrammingtheCo-evolutionaryPSO
    RunningthePSOImplementation
    Summary
    Exercises

    chapterfiveNeuralNetworkConceptsandParadigms
    NeuralNetworkHistory
    WhereDidNeuralNetworksGetTheirName?
    TheAgeofCamelot
    TheDarkAge
    TheRenaissance
    TheAgeofNeoconnectionism
    TheAgeofComputationalIntelligence
    WhatNeuralNetworksAreandWhyTheyAreUseful
    NeuralNetworkComponentsandTerminology
    Terminology
    InputandOutputPatterns
    NetworkWeights
    ProcessingElements
    ProcessingElementActivationFunctions
    NeuralNetworkTopologies
    Terminology
    Two-layerNetworks
    MultilayerNetworks
    NeuralNetworkAdaptation
    Terminology
    HebbianAdaptation
    CompetitiveAdaptation
    MultilayerErrorCorrectionAdaptation
    SummaryofAdaptationProcedures
    ComparingNeuralNetworksandOtherInformationProcessingMethods
    StochasticApproximation
    KalmanFilters
    LinearandNonlinearRegression
    Correlation
    BayesClassification
    VectorQuantization
    RadialBasisFunctions
    ComputationalIntelligence
    Preprocessing
    SelectingTraining,Test,andValidationDatasets
    PreparingData
    Postprocessing
    DenormalizationofOutputData
    Summary
    Exercises

    chaptersixNeuralNetworkImplementations
    ImplementationIssues
    Topology
    Back-propagationNetworkInitializationandNormalization
    LearningVectorQuantizerNetworkInitializationandNormalization
    FeedforwardCalculationsfortheBack-propagationNetwork
    FeedforwardCalculationsfortheLVQ-INet
    Back-propagationSupervisedAdaptationbyErrorBack-propagation
    LVQUnsupervisedAdaptationCalculations
    TheLVQSupervisedAdaptationAlgorithm
    IssuesinEvolvingNeuralNetworks
    AdvantagesandDisadvantagesofPreviousEvolutionaryApproaches
    EvolvingNeuralNetworkswithParticleSwarmOptimization
    Back-propagationImplementation
    ProgrammingaBack-propagationNeuralNetwork
    RunningtheBack-propagationImplementation
    TheKohonenNetworkImplementations
    ProgrammingtheLearningVectorQuantizer
    RunningtheLVQImplementation
    ProgrammingtheSelf-organizingFeatureMap
    RunningtheSOFMImplementation
    EvolutionaryBack-propagationNetworkImplementation
    ProgrammingtheEvolutionaryBack-propagationNetwork
    RunningtheEvolutionaryBack-propagationNetwork
    Summary
    Exercises

    chaptersevenFuzzySystemsConceptsandParadigms
    History
    FuzzySetsandFuzzyLogic
    Logic,FuzzyandOtherwise
    FuzzinessIsNotProbability
    TheTheoryofFuzzySets
    FuzzySetMembershipFunctions
    LinguisticVariables
    LinguisticHedges
    ApproximateReasoning
    ParadoxesinFuzzyLogic
    EqualityofFuzzySets
    Containment
    NOT:TheComplementofaFuzzySet
    AND:TheIntersectionofFuzzySets
    OR:TheUnionofFuzzySets
    CompensatoryOperators
    FuzzyRules
    Fuzzification
    FuzzyRulesFireinParallel
    Defuzzification
    OtherDefuzzificationMethods
    MeasuresofFuzziness
    DevelopingaFuzzyController
    WhyFuzzyControl
    AFuzzyController
    BuildingaMamdani-typeFuzzyController
    FuzzyControllerOperation
    Takagi-Sugeno-KangMethod
    Summary
    Exercises

    chaptereightFuzzySystemsImplementations
    ImplementationIssues
    FuzzyRuleRepresentation
    EvolutionaryDesignofaFuzzyRuleSystem
    AnObject-orientedLanguage:C++
    FuzzyRuleSystemImplementation
    ProgrammingFuzzyRuleSystems
    RunningtheFuzzyRuleSystem
    IrisDatasetApplication
    EvolvingFuzzyRuleSystems
    ProgrammingtheEvolutionaryFuzzyRuleSystem
    RunningtheEvolutionaryFuzzyRuleSystem
    Summary
    Exercises

    chapternineComputationalIntelligenceImplementations
    ImplementationIssues
    AdaptationofGeneticAlgorithms
    FuzzyAdaptation
    KnowledgeElicitation
    FuzzyEvolutionaryFuzzyRuleSystemImplementation
    ProgrammingtheFuzzyEvolutionaryFuzzyRuleSystem
    RunningtheFuzzyEvolutionaryFuzzyRuleSystem
    ChoosingtheBestTools
    StrengthsandWeaknesses
    ModelingandOptimization
    PracticalIssues
    ApplyingComputationalIntelligencetoDataMining
    AnExampleDataMiningSystem
    Summary
    Exercises

    chaptertenPerformanceMetrics
    GeneralIssues
    SelectingGoldStandards
    PartitioningthePatternsforTraining,Testing,andValidation
    CrossValidation
    FitnessandFitnessFunctions
    ParametricandNonparametricStatistics
    PercentCorrect
    AverageSum-squaredError
    AbsoluteError
    NormalizedError
    EvolutionaryAlgorithmEffectivenessMetrics
    Mann-WhitneyUTest
    ReceiverOperatingCharacteristicCurves
    RecallandPrecision
    OtherROC-relatedMeasures
    ConfusionMatrices
    Chi-squareTest
    Summary
    Exercises

    chapterelevenAnalysisandExplanation
    SensitivityAnalysis
    RelationFactors
    ZuradaSensitivityAnalysis
    EvolutionaryComputationSensitivityAnalysis
    HintonDiagrams
    ComputationalIntelligenceToolsforExplanationFacilities
    ExplanationFacilityRequirements
    NeuralNetworkExplanation
    FuzzyExpertSystemExplanation
    EvolutionaryComputationToolsforExplanation
    AnExampleNeuralNetworkExplanationFacility
    Summary
    Exercises
    Bibliography
    Index
    AbouttheAuthors
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