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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50933

Title: Harmonic neural networks for on-line learning vector quantisation
Authors: J.-H. Wang
J.-D. Rau
C.-Y. Peng
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: neural nets
unsupervised learning
vector quantisation
online operation
image coding
Date: 2000-07
Issue Date: 2018-11-01T01:52:24Z
Publisher: IEE Proceedings-Vision, Image and Signal Processing
Abstract: Abstract: A self-creating harmonic neural network (HNN) trained with a competitive algorithm effective for on-line learning vector quantisation is presented. It is shown that by employing dual resource counters to record the activity of each node during the training process, the equi-error and equi-probable criteria can be harmonised. Training in HNNs is smooth and incremental, and it not only achieves the biologically plausible on-line learning property, but it can also avoid the stability-plasticity dilemma, the dead-node problem, and the deficiency of the local minimum. Characterising HNNs reveals the great controllability of HNNs in favouring one criterion over the other, when faced with a must-choose situation between equi-error and equi-probable. Comparison studies on learning vector quantisation involving stationary and non-stationary, structured and non-structured inputs demonstrate that the HNN outperforms other competitive networks in terms of quantisation error, learning speed and codeword search efficiency.
Relation: 147(5) pp.485-492
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50933
Appears in Collections:[電機工程學系] 期刊論文

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