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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50929

Title: Learning temporal sequences using dual-weight neurons
Authors: Jung-Hua Wang
Ming-Chieh Tsai
Wen-Sheng Su
Contributors: 國立臺灣海洋大學:電機工程學系
Date: 2001-05
Issue Date: 2018-11-01T01:21:47Z
Publisher: Journal of Chinese Institute of Engineers
Abstract: Abstract: This paper considers the use of neural networks (NN's) in learn-ing temporal sequence recognition and reproduction for which the se-quence degree is unknown. This approach uses the output ambiguity to train the network without the need to assume or construct a separate model for the input sequence degree. First we introduce a primitive network called the DNN, comprising a plurality of dual-weight (DN) neurons. Each neuron is linked to other neurons by a long-term excita-tory weight and a short-term inhibitory weight. Fast learning is made possible by employing a two-pass training rule to encode the temporal distance between two arbitrary pattern occurrences. The resulting DNN is then extended into a more generalized model, namely the DNN2. By incorporating the two-pass rule and a self-organizing algorithm, the DNN2 can achieve autonomous temporal sequence recognition and reproduction. Using training efficiency and hardware complexity criteria, the DNN networks are also contrasted with the work of Wang and Yuwono (1995).
Relation: 24(3)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50929
Appears in Collections:[電機工程學系] 期刊論文

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