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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50931

Title: Towards optimizing a self-creating neural network
Authors: Jung-Hua Wang
Jen-Da Rau
Chung-Yun Peng
Contributors: 國立臺灣海洋大學:電機工程學系
Date: 2000-08
Issue Date: 2018-11-01T01:43:39Z
Publisher: IEEE Trans. on Systems, Man, and Cybernetics-Part B
Abstract: Abstract: This paper optimizes the performance of the growing cell structures (GCS) model in learning topology and vector quantization. Each node in GCS is attached with a resource counter. During the competitive learning process, the counter of the best-matching node is increased by a defined resource measure after each input presentation, and then all resource counters are decayed by a factor /spl alpha/. We show that the summation of all resource counters conserves. This conservation principle provides useful clues for exploring important characteristics of GCS, which in turn provide an insight into how the GCS can be optimized. In the context of information entropy, we show that performance of GCS in learning topology and vector quantization can be optimized by using /spl alpha/=0 incorporated with a threshold-free node-removal scheme, regardless of input data being stationary or nonstationary. The meaning of optimization is twofold: (1) for learning topology, the information entropy is maximized in terms of equiprobable criterion and (2) for leaning vector quantization, the use is minimized in terms of equi-error criterion.
Relation: 30(4) pp.586-593
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50931
Appears in Collections:[電機工程學系] 期刊論文

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