| 000 | 09245nam a2201465 i 4500 | ||
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| 001 | 7547467 | ||
| 003 | IEEE | ||
| 005 | 20220712205932.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 160920s2008 nju ob 001 eng d | ||
| 019 |
_a954045921 _a957615443 _a958350488 |
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| 020 | _a1119214408 | ||
| 020 |
_a9781119214403 _qelectronic |
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| 020 |
_z9781119214342 _qprint |
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| 020 |
_z9781119214359 _qelectronic bk. |
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_z1119214351 _qelectronic bk. |
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| 020 |
_z9781119214366 _qelectronic bk. |
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| 020 |
_z111921436X _qelectronic bk. |
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| 020 | _z9781119214403 | ||
| 020 | _z1119214343 | ||
| 035 | _a(CaBNVSL)mat07547467 | ||
| 035 | _a(IDAMS)0b00006485691f5c | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.76.E95 _bK45 2016eb |
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| 082 | 0 | 4 |
_a006.33 _223 |
| 100 | 1 |
_aKeller, James M., _eauthor. _928859 |
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| 245 | 1 | 0 |
_aFundamentals of computational intelligence : _bneural networks, fuzzy systems, and evolutionary computation / _cJames M. Keller, Derong Liu, David B. Fogel. |
| 264 | 1 |
_aHoboken, New Jersey : _bIEEE Press/Wiley, _c2016. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2016] |
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| 300 | _a1 PDF (400 pages). | ||
| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aIEEE Press series on computational intelligence | |
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 8 | _aChapter 6: Basic Fuzzy Set Theory6.1 Introduction; 6.2 A Brief History; 6.3 Fuzzy Membership Functions and Operators; 6.3.1 Membership Functions; 6.3.2 Basic Fuzzy Set Operators; 6.4 Alpha-Cuts, the Decomposition Theorem, and the Extension Principle; 6.5 Compensatory Operators; 6.6 Conclusions; Exercises; Chapter 7: Fuzzy Relations and Fuzzy Logic Inference; 7.1 Introduction; 7.2 Fuzzy Relations and Propositions; 7.3 Fuzzy Logic Inference; 7.4 Fuzzy Logic for Real-Valued Inputs; 7.5 Where Do the Rules Come From?; 7.6 Chapter Summary; Exercises; Chapter 8: Fuzzy Clustering and Classification | |
| 505 | 0 | _aFundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation; Table of Contents; Acknowledgments; Chapter 1: Introduction to Computational Intelligence; 1.1 Welcome to Computational Intelligence; 1.2 What Makes This Book Special; 1.3 What This Book Covers; 1.4 How to Use This Book; 1.5 Final Thoughts Before You Get Started; Part I: Neural Networks; Chapter 2: Introduction and Single-Layer Neural Networks; 2.1 Short History of Neural Networks; 2.2 Rosenblatt's Neuron; 2.3 Perceptron Training Algorithm; 2.3.1 Test Problem | |
| 505 | 8 | _a2.3.2 Constructing Learning Rules2.3.3 Unified Learning Rule; 2.3.4 Training Multiple-Neuron Perceptrons; 2.3.4.1 Problem Statement; 2.4 The Perceptron Convergence Theorem; 2.5 Computer Experiment Using Perceptrons; 2.6 Activation Functions; 2.6.1 Threshold Function; 2.6.2 Sigmoid Function; Exercises; Chapter 3: Multilayer Neural Networks and Backpropagation; 3.1 Universal Approximation Theory; 3.2 The Backpropagation Training Algorithm; 3.2.1 The Description of the Algorithm; 3.2.2 The Strategy for Improving the Algorithm; 3.2.3 The Design Procedure of the Algorithm | |
| 505 | 8 | _a3.3 Batch Learning and Online Learning3.3.1 Batch Learning; 3.3.2 Online Learning; 3.4 Cross-Validation and Generalization; 3.4.1 Cross-Validation; 3.4.2 Generalization; 3.4.3 Convolutional Neural Networks; 3.5 Computer Experiment Using Backpropagation; Exercises; Chapter 4: Radial-Basis Function Networks; 4.1 Radial-Basis Functions; 4.2 The Interpolation Problem; 4.3 Training Algorithms for Radial-Basis Function Networks; 4.3.1 Layered Structure of a Radial-Basis Function Network; 4.3.2 Modification of the Structure of RBF Network; 4.3.3 Hybrid Learning Process; 4.4 Universal Approximation | |
| 505 | 8 | _a4.5 Kernel RegressionExercises; Chapter 5: Recurrent Neural Networks; 5.1 The Hopfield Network; 5.2 The Grossberg Network; 5.2.1 Basic Nonlinear Model; 5.2.2 Two-Layer Competitive Network; 5.2.2.1 Layer 1; 5.2.2.2 Layer 2; 5.2.2.3 Learning Law; Basic Nonlinear Model: Leaky Integrator; Layer 1; Layer 2; 5.3 Cellular Neural Networks; 5.4 Neurodynamics and Optimization; 5.5 Stability Analysis of Recurrent Neural Networks; 5.5.1 Stability Analysis of the Hopfield Network; 5.5.2 Stability Analysis of the Cohen-Grossberg Network; Exercises; Part II: Fuzzy Set Theory and Fuzzy Logic | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aProvides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. . Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks. Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals. Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence. Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aOnline resource; title from PDF title page (EBSCO, viewed August 12, 2016) | ||
| 650 | 0 |
_aExpert systems (Computer science) _93392 |
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| 650 | 7 |
_aExpert systems (Computer science) _2fast _93392 |
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| 655 | 4 |
_aElectronic books. _93294 |
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| 695 | _aAirports | ||
| 695 | _aAxons | ||
| 695 | _aBackpropagation | ||
| 695 | _aBackpropagation algorithms | ||
| 695 | _aBiological neural networks | ||
| 695 | _aBiological system modeling | ||
| 695 | _aBirds | ||
| 695 | _aBrain modeling | ||
| 695 | _aClassification algorithms | ||
| 695 | _aClustering algorithms | ||
| 695 | _aComplexity theory | ||
| 695 | _aComputational modeling | ||
| 695 | _aComputers | ||
| 695 | _aCorrelation | ||
| 695 | _aData models | ||
| 695 | _aData visualization | ||
| 695 | _aDecision making | ||
| 695 | _aDelay effects | ||
| 695 | _aDensity measurement | ||
| 695 | _aDesign methodology | ||
| 695 | _aEvolution (biology) | ||
| 695 | _aEvolutionary computation | ||
| 695 | _aExplosives | ||
| 695 | _aExtraterrestrial measurements | ||
| 695 | _aFoot | ||
| 695 | _aFuzzy logic | ||
| 695 | _aFuzzy set theory | ||
| 695 | _aFuzzy sets | ||
| 695 | _aFuzzy systems | ||
| 695 | _aGenetics | ||
| 695 | _aHair | ||
| 695 | _aHistory | ||
| 695 | _aHopfield neural networks | ||
| 695 | _aImage recognition | ||
| 695 | _aInterpolation | ||
| 695 | _aLinear programming | ||
| 695 | _aMathematical model | ||
| 695 | _aMeasurement | ||
| 695 | _aMeasurement uncertainty | ||
| 695 | _aMedical services | ||
| 695 | _aMultilayer perceptrons | ||
| 695 | _aNeural networks | ||
| 695 | _aNeurons | ||
| 695 | _aNonhomogeneous media | ||
| 695 | _aOptimization | ||
| 695 | _aOrganisms | ||
| 695 | _aParticle swarm optimization | ||
| 695 | _aPattern recognition | ||
| 695 | _aPhase change materials | ||
| 695 | _aPragmatics | ||
| 695 | _aPredictive models | ||
| 695 | _aProblem-solving | ||
| 695 | _aRadial basis function networks | ||
| 695 | _aRandom variables | ||
| 695 | _aSearch problems | ||
| 695 | _aSociology | ||
| 695 | _aStandards | ||
| 695 | _aStatistics | ||
| 695 | _aSurface contamination | ||
| 695 | _aSurface morphology | ||
| 695 | _aSurface treatment | ||
| 695 | _aTraining | ||
| 695 | _aTraining data | ||
| 695 | _aUncertainty | ||
| 695 | _aUnsupervised learning | ||
| 695 | _aUrban areas | ||
| 695 | _aVegetation | ||
| 700 | 1 |
_aLiu, Derong, _eauthor. _928860 |
|
| 700 | 1 |
_aFogel, David B., _eauthor. _923634 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _928861 |
|
| 710 | 2 |
_aWiley, _epublisher. _928862 |
|
| 776 | 0 | 8 |
_iPrint version: _aKeller, James M. _tFundamentals of Computational Intelligence : Neural Networks, Fuzzy Systems, and Evolutionary Computation _dHoboken : Wiley,c2016 _z9781119214403 |
| 830 | 0 |
_aIEEE series on computational intelligence. _94752 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7547467 |
| 942 | _cEBK | ||
| 999 |
_c74455 _d74455 |
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