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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/38094

Authors: Po-Lun Chang
Ying-Kuei Yang
Horng-Lin Shieh
Fei-Hu Hsieh
Mu-Der Jeng
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Grey relational grade
Learning interference
Credit apportionment
Date: 2010
Issue Date: 2016-08-03T02:31:36Z
Publisher: International Journal of Innovative Computing, Information and Control
Abstract: Abstract: Fast Learning and accurate convergence are the two issues to be most concerned
in the research area of a Cerebellar Model Articulation Controller (CMAC). This
paper investigates to incorporate grey relational analysis with number of training iterations
to obtain an adaptive and appropriate learning rate for each input state to improve
the CMAC stability and convergence. Additionally, this paper also proposes that the
amount of weight adjustment to a memory cell of an addressed hyper cube must be relational
to the trained input area, grey relational grade in the current training iteration
and the inverse of the number of learning times to minimize the learning interference.
A credit apportionment approach is thus derived for implementing this idea to achieve
fast and accurate learning performance. The results of the experiments conducted in this
study clearly demonstrate that the proposed approach provides a more accurate learning
mechanism and faster convergence.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/38094
Appears in Collections:[電機工程學系] 期刊論文

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