Please use this identifier to cite or link to this item:
|Title: ||GREY RELATIONAL ANALYSIS BASED APPROACH FOR CMAC LEARNING|
|Authors: ||Po-Lun Chang|
|Keywords: ||Grey relational grade|
|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.
|Appears in Collections:||[電機工程學系] 期刊論文|
Files in This Item:
|Grey Relational Analysis Based Approach for CMAC Learning.pdf||31Kb||Adobe PDF||68||View/Open|
All items in NTOUR are protected by copyright, with all rights reserved.