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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50962

Title: A Novel Self-Creating Neural Network for Learning Vector Quantization
Authors: Jung-Hua Wang
Chung-Yun Peng
Contributors: 國立臺灣海洋大學電機工程學系
Date: 2000-04
Issue Date: 2018-11-02
Publisher: Neural Processing Letters
Abstract: Abstract: This paper presents a novel self-creating neural network scheme which employs two resource counters to record network learning activity. The proposed scheme not only achieves the biologically plausible learning property, but it also harmonizes equi-error and equi-probable criteria. The training process is smooth and incremental: it not only avoids the stability-and-plasticity dilemma, but also overcomes the dead-node problem and the deficiency of local minimum. Comparison studies on learning vector quantization involving stationary and non-stationary, structured and non-structured inputs demonstrate that the proposed scheme outperforms other competitive networks in terms of quantization error, learning speed, and codeword search efficiency.
Relation: 11(2) pp.139-151
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50962
Appears in Collections:[電機工程學系] 博碩士論文

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