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Titlebook: Structure Level Adaptation for Artificial Neural Networks; Tsu-Chang Lee Book 1991 Springer Science+Business Media New York 1991 artificia

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发表于 2025-3-21 19:56:48 | 显示全部楼层 |阅读模式
书目名称Structure Level Adaptation for Artificial Neural Networks
编辑Tsu-Chang Lee
视频video
丛书名称The Springer International Series in Engineering and Computer Science
图书封面Titlebook: Structure Level Adaptation for Artificial Neural Networks;  Tsu-Chang Lee Book 1991 Springer Science+Business Media New York 1991 artificia
描述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 M
出版日期Book 1991
关键词artificial neural network; convergence; neural networks; simulation
版次1
doihttps://doi.org/10.1007/978-1-4615-3954-4
isbn_softcover978-1-4613-6765-9
isbn_ebook978-1-4615-3954-4Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 1991
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发表于 2025-3-21 22:54:37 | 显示全部楼层
Competitive Signal Clustering Networks,gs to the category of . (see Definition 2.4 for the general definition), in which each neuron is assigned a position in a lattice. The lattice is useful for containing the information about the structural relation between neurons in the network. Through the competitive mechanisms between neurons in
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Application Example: An Adaptive Neural Network Source Coder,e SPAN as . that can grow from scratch to follow the statistics of source signals, capture the local context of the source signal space, and map onto the structure of the network. As a result, when the statistics of the source signals change, the network can dynamically modify its structure to follo
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Multi-Layer Feed-Forward Networks,Recent developments in neural network theory show that multi-layer feed-forward neural networks with one hidden layer of neurons can be used to approximate any multi-dimensional function to any desired accuracy, if a suitable number of neurons are included in the hidden layer and the correct interconnection weight values can be found [28].
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Conclusions,The . shown in Figure 1.3 outlines the contributions of this monograph. To be more specific, this study has achieved the following:
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