By Tsu-Chang Lee
63 three. 2 functionality point variation sixty four three. three Parameter point model. sixty seven three. four constitution point variation 70 three. four. 1 Neuron iteration . 70 three. four. 2 Neuron Annihilation seventy two three. five Implementation . . . . . seventy four three. 6 An Illustrative instance seventy seven three. 7 precis . . . . . . . . seventy nine four aggressive sign Clustering Networks ninety three four. 1 advent. . ninety three four. 2 simple constitution ninety four four. three functionality point variation ninety six four. four Parameter point model . a hundred and one four. five constitution point version 104 four. five. 1 Neuron iteration method 107 four. five. 2 Neuron Annihilation and Coalition procedure 114 four. five. three Structural Relation Adjustment. 116 four. 6 Implementation . . 119 four. 7 Simulation effects 122 four. eight precis . . . . . 134 five program instance: An Adaptive Neural community resource Coder a hundred thirty five five. 1 advent. . . . . . . . . . a hundred thirty five five. 2 Vector Quantization challenge 136 five. three VQ utilizing Neural community Paradigms 139 Vlll five. three. 1 uncomplicated houses . a hundred and forty five. three. 2 quick Codebook seek strategy 141 five. three. three direction Coding approach. . . . . . . 143 five. three. four functionality comparability . . . . a hundred and forty four five. three. five Adaptive SPAN Coder/Decoder 147 five. four precis . . . . . . . . . . . . . . . . . 152 6 Conclusions a hundred and fifty five 6. 1 Contributions one hundred fifty five 6. 2 strategies 157 A Mathematical historical past 159 A. 1 Kolmogorov's Theorem . one hundred sixty A. 2 Networks with One Hidden Layer are adequate 161 B Fluctuated Distortion degree 163 B. 1 degree development . 163 B. 2 The Relation among Fluctuation and blunder 166 C SPAN Convergence concept 171 C. 1 Asymptotic price of Wi 172 C. 2 power functionality . .
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Extra resources for Structure Level Adaptation for Artificial Neural Networks
To propose a method to calculate the minimum delay required for a neural net to propagate the effects of input signals to the output port. • to formalize the multi-level adaptation process for neural network systems. In particular, I defined the concept of "Structure Level 23 24 CHAPTER 2. BASIC FRAMEWORK Neural Networks", which are networks capable of changing their structures autonomously. In defining this concept, I adopt two basic viewpoints: 1. Information Representation Point of View: I define the items in my conceptual structure based on an information representation point of view.
The corollaries above reveal a very important criterion for neural network structure design: when putting neurons together into a network structure, if the signal flow graph (Le. the function interconnection graph) is forestizable, then the computation results of the network can appear on the output port at the same time index as the input signals; otherwise a delay function must be inserted into each cycle in the graph. In order to transform a state inconsistent network into a state consistent one, we need to identify all the cycles in the network.
BASIC FRAMEWORK 48 1/, called the Structure State Trasition Function (SSTF), is a mapping which takes Gl*],In(G)l*] ,Rfb(G)l*],Jl*] and Cinl*] to generate J[* + 1]. The vector Cin' called the Received Structure Control Vector (RSCV), contains the signals received from the environment 18 (other than the F-level and L-Ievel signals) in order to control the S-level adaptation process of the network. J, called the Structure Control State (SCS) of E, is a structure of variables used to control the S-level adaptation process.