| 000 | 01015camuuu200277 a 4500 | |
| 001 | 000000583511 | |
| 003 | OCoLC | |
| 005 | 19980310132903.0 | |
| 008 | 910131s1991 maua b 001 0 eng | |
| 010 | ▼a 91002251 //r91 | |
| 015 | ▼a GB91-43538 | |
| 019 | ▼a 26316055 | |
| 020 | ▼a 0792391519 | |
| 040 | ▼a DLC ▼c DLC ▼d UKM | |
| 049 | ▼a ACSL ▼l 121030355 ▼l 121030354 | |
| 050 | 0 0 | ▼a QA76.87 ▼b .L443 1991 |
| 082 | 0 0 | ▼a 006.3 ▼2 20 |
| 090 | ▼a 006.3 ▼b L481s | |
| 100 | 1 | ▼a Lee, Tsu-Chang, ▼d 1961- |
| 245 | 1 0 | ▼a Structure level adaptation for artificial neural networks / ▼c by Tsu-Chang Lee ; foreword by Joseph W. Goodman. |
| 260 | ▼a Boston : ▼b Kluwer Academic Publishers, ▼c c1991. | |
| 300 | ▼a xix, 212 p. : ▼b ill. ; ▼c 24 cm. | |
| 440 | 4 | ▼a The Kluwer international series in engineering and computer science ; ▼v SECS 133. ▼p Knowledge representation, learning, and expert systems |
| 504 | ▼a Includes bibliographical references (p. 189-205) and index. | |
| 650 | 0 | ▼a Neural networks (Computer science) |
| 653 | 0 | ▼a Computers ▼a Networks |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 L481s | 등록번호 121030354 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 2 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 L481s | 등록번호 121030355 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
63 3. 2 Function Level Adaptation 64 3. 3 Parameter Level Adaptation. 67 3. 4 Structure Level Adaptation 70 3. 4. 1 Neuron Generation . 70 3. 4. 2 Neuron Annihilation 72 3. 5 Implementation . . . . . 74 3. 6 An Illustrative Example 77 3. 7 Summary . . . . . . . . 79 4 Competitive Signal Clustering Networks 93 4. 1 Introduction. . 93 4. 2 Basic Structure 94 4. 3 Function Level Adaptation 96 4. 4 Parameter Level Adaptation . 101 4. 5 Structure Level Adaptation 104 4. 5. 1 Neuron Generation Process 107 4. 5. 2 Neuron Annihilation and Coalition Process 114 4. 5. 3 Structural Relation Adjustment. 116 4. 6 Implementation . . 119 4. 7 Simulation Results 122 4. 8 Summary . . . . . 134 5 Application Example: An Adaptive Neural Network Source Coder 135 5. 1 Introduction. . . . . . . . . . 135 5. 2 Vector Quantization Problem 136 5. 3 VQ Using Neural Network Paradigms 139 Vlll 5. 3. 1 Basic Properties . 140 5. 3. 2 Fast Codebook Search Procedure 141 5. 3. 3 Path Coding Method. . . . . . . 143 5. 3. 4 Performance Comparison . . . . 144 5. 3. 5 Adaptive SPAN Coder/Decoder 147 5. 4 Summary . . . . . . . . . . . . . . . . . 152 6 Conclusions 155 6. 1 Contributions 155 6. 2 Recommendations 157 A Mathematical Background 159 A. 1 Kolmogorov's Theorem . 160 A. 2 Networks with One Hidden Layer are Sufficient 161 B Fluctuated Distortion Measure 163 B. 1 Measure Construction . 163 B. 2 The Relation Between Fluctuation and Error 166 C SPAN Convergence Theory 171 C. 1 Asymptotic Value of Wi 172 C. 2 Energy Function . .
63 3. 2 Function Level Adaptation 64 3. 3 Parameter Level Adaptation. 67 3. 4 Structure Level Adaptation 70 3. 4. 1 Neuron Generation . 70 3. 4. 2 Neuron Annihilation 72 3. 5 Implementation . . . . . 74 3. 6 An Illustrative Example 77 3. 7 Summary . . . . . . . . 79 4 Competitive Signal Clustering Networks 93 4. 1 Introduction. . 93 4. 2 Basic Structure 94 4. 3 Function Level Adaptation 96 4. 4 Parameter Level Adaptation . 101 4. 5 Structure Level Adaptation 104 4. 5. 1 Neuron Generation Process 107 4. 5. 2 Neuron Annihilation and Coalition Process 114 4. 5. 3 Structural Relation Adjustment. 116 4. 6 Implementation . . 119 4. 7 Simulation Results 122 4. 8 Summary . . . . . 134 5 Application Example: An Adaptive Neural Network Source Coder 135 5. 1 Introduction. . . . . . . . . . 135 5. 2 Vector Quantization Problem 136 5. 3 VQ Using Neural Network Paradigms 139 Vlll 5. 3. 1 Basic Properties . 140 5. 3. 2 Fast Codebook Search Procedure 141 5. 3. 3 Path Coding Method. . . . . . . 143 5. 3. 4 Performance Comparison . . . . 144 5. 3. 5 Adaptive SPAN Coder/Decoder 147 5. 4 Summary . . . . . . . . . . . . . . . . . 152 6 Conclusions 155 6. 1 Contributions 155 6. 2 Recommendations 157 A Mathematical Background 159 A. 1 Kolmogorov's Theorem . 160 A. 2 Networks with One Hidden Layer are Sufficient 161 B Fluctuated Distortion Measure 163 B. 1 Measure Construction . 163 B. 2 The Relation Between Fluctuation and Error 166 C SPAN Convergence Theory 171 C. 1 Asymptotic Value of Wi 172 C. 2 Energy Function . .
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목차
1 Introduction.- 1.1 Background.- 1.2 Neural Network Paradigms.- 1.3 The Frame Problem in Artificial Neural Networks.- 1.4 Approach.- 1.5 Overview of This Book.- 2 Basic Framework.- 2.1 Introduction.- 2.2 Formal Neurons.- 2.3 Formal Neural Networks.- 2.4 Multi-Level Adaptation Formalism.- 2.5 Activity-Based Structural Adaptation.- 2.5.1 Neuron generation.- 2.5.2 Neuron Annihilation.- 2.5.3 Structural Relationship Modification.- 2.6 Summary.- 3 Multi-Layer Feed-Forward Networks.- 3.1 Introduction.- 3.2 Function Level Adaptation.- 3.3 Parameter Level Adaptation.- 3.4 Structure Level Adaptation.- 3.4.1 Neuron Generation.- 3.4.2 Neuron Annihilation.- 3.5 Implementation.- 3.6 An Illustrative Example.- 3.7 Summary.- 4 Competitive Signal Clustering Networks.- 4.1 Introduction.- 4.2 Basic Structure.- 4.3 Function Level Adaptation.- 4.4 Parameter Level Adaptation.- 4.5 Structure Level Adaptation.- 4.5.1 Neuron Generation Process.- 4.5.2 Neuron Annihilation and Coalition Process.- 4.5.3 Structural Relation Adjustment.- 4.6 Implementation.- 4.7 Simulation Results.- 4.8 Summary.- 5 Application Example: An Adaptive Neural Network Source Coder.- 5.1 Introduction.- 5.2 Vector Quantization Problem.- 5.3 VQ Using Neural Network Paradigms.- 5.3.1 Basic Properties.- 5.3.2 Fast Codebook Search Procedure.- 5.3.3 Path Coding Method.- 5.3.4 Performance Comparison.- 5.3.5 Adaptive SPAN Coder/Decoder.- 5.4 Summary.- 6 Conclusions.- 6.1 Contributions.- 6.2 Recommendations.
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