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| 005 | 19980603112333.0 | |
| 008 | 930104s1993 njua b 001 0 eng | |
| 010 | ▼a 93000026 | |
| 015 | ▼a GB93-40838 | |
| 020 | ▼a 0133689034 | |
| 040 | ▼a DLC ▼c DLC ▼d UKM | |
| 049 | 1 | ▼l 121003454 ▼f 과학 |
| 050 | 0 0 | ▼a QA76.87 ▼b .C45 1993 |
| 082 | 0 0 | ▼a 006.3 ▼2 20 |
| 090 | ▼a 006.3 ▼b C525n | |
| 100 | 1 | ▼a Chester, Michael. |
| 245 | 1 0 | ▼a Neural networks : ▼b a tutorial / ▼c Michael Chester. |
| 260 | ▼a Englewood Cliffs, N.J. : ▼b PTR Prentice Hall, ▼c c1993. | |
| 300 | ▼a viii, 182 p. : ▼b ill. ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references (p. 171-179) and index. | |
| 650 | 0 | ▼a Neural networks (Computer science). |
| 653 | 0 | ▼a Computers ▼a Networks |
소장정보
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|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 C525n | 등록번호 121003454 (20회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
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|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.3 C525n | 등록번호 151019825 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
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| No. 3 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.3 C525n | 등록번호 452094334 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book bridges the gap between the highly theoretical level of research in neural networks and the practical, analytical level encountered by those who are not specialised in the neural field. It covers the taxonomy of network paradigms and interrelates the paradigms in a systematic fashion, explores practical implementation in terms of applications and discusses neural products that are in development or on the market: chips, printed circuit boards, systems and software.
정보제공 :
목차
CONTENTS Preface = vi Acknowledgements = viii 1 Structure of an Artificial Neural Network = 1 Artificial Neurons = 1 Taxonomies = 6 Neural Network Logic = 11 2 Applications = 12 Robots = 16 Implementations = 17 3 The Early Years = 19 The Perceptron = 19 The LMS Law = 26 4 The Hopfield Net = 28 Symbolic Operations = 31 Network Capacity and False Exemplars = 33 The Lyapunov Condition = 34 A Continuous Model = 35 Hopfield Network Applications = 37 5 Self-Organizing Maps = 42 Applications of the Self-Organizing Map = 45 Other Studies of Topological Mapping = 47 A Note on "Selecting a Winner" = 48 6 Backpropagation = 50 Error Surface = 51 Generalized Delta Rule = 53 Synaptic Changes in Hidden Layers = 56 Backpropagation Summary = 57 A Hyperplane Model = 57 A Neural Speech Synthesizer = 59 Other Applications = 61 Limitations and Remedies = 62 Recurrent Backpropagation = 63 7 Learning Laws and Continued Taxonomy = 66 Learning Laws = 67 Unsupervised Learning Laws = 68 8 Adaptive Resonance = 71 The ART Paradigms = 75 A Note on Parsimony = 81 9 Bidirectional Associative Memory = 82 TAM = 86 MAM = 86 ABAM = 87 The Noise-Saturation Dilemma = 89 ABAM: Implementation = 90 CABAM = 91 RABAM = 92 10 Variations on Neural Themes = 95 The Boltzmann Machine, a Probabilistic Model = 95 The Hamming Network = 98 Brain-State-in-a-Box(BSB) = 101 The Neocognitron = 103 Restricted Coulomb Energy = 107 Dystal, a Biological Model = 114 Drive Reinforcement Model = 117 11 Fuzzy Theroy = 119 Functions of Fuzzy Vectors = 120 Fuzziness versus Aristotle = 122 A Fuzzy Neural Underwater Sound Detector = 125 A Fuzzy Truck Driver = 130 12 Integrated Circuits = 135 Building Block and Thin-Film Approaches = 136 Adaptive Synapses Based on MNOS and CCD Technologies = 137 An All-Digital Neural Network = 140 A Silicon Retina = 145 13 Optical Neural Nets = 148 Optical Techniques = 149 Holograms = 150 14 The Evolution of Intelligence = 155 Genetics = 155 Modular Neural Evolution = 158 Natural Selection = 159 Simulated Imagination = 162 Appendix: Mathematical Notation = 167 References and Bibligraphy = 171 Index = 180
