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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50963

Title: On the characteristics of growing cell structures (GCS) neural network
Authors: Jung-Hua Wang
Wei-Der Sun
Contributors: 國立臺灣海洋大學電機工程學系
Keywords: self-developing neural network
competitive learning
race-condition, topology
equiprobable criterion
chain-reaction effect
Date: 1999-10
Issue Date: 2018-11-02
Publisher: Neural Processing Letters
Abstract: Abstract: In this paper, a self-developing neural network model, namely the Growing Cell Structures
(GCS) is characterized. In GCS each node (or cell) is associated with a local resource counter τ (t ).
We show that GCS has the conservation property by which the summation of all resource counters
always equals s(1−α)
α , thereby s is the increment added to τ (t ) of the wining node after each input
presentation and α(0 <α< 1.0) is the forgetting (i.e., decay) factor applied to τ (t ) of non-wining
nodes. The conservation property provides an insight into how GCS can maximize information
entropy. The property is also employed to unveil the chain-reaction effect and race-condition which
can greatly influence the performance of GCS. We show that GCS can perform better in terms of
equi-probable criterion if the resource counters are decayed by a smaller α.
Relation: 10(2)pp.139-149
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50963
Appears in Collections:[電機工程學系] 期刊論文

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