Presentation 1995/10/19
A Design Technique of Cellular Neural Networks : Optimaization methods constrained by inequality conditions
Koji NAKAI, Akio USHIDA,
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Abstract(in English) We discuss optimization problems constrained by an excess number of linear inequalities, which come from a design of cellular neural networks. In this cases, we introduce a quadratic programming to design the robust template which can find the optimum solution in a closed convex domain only if all of the linear equations constitute the boundaries. However, if it has an excess number of the inequalities, the quadratic programing will sometimes fail to get the optimum solution. In this paper, we propose 3 techniques of simplex method, sign test method and penalty function method. Although first two methods can get the optimum solution, they are inefficiencies for the large systems. The last one can efficiently find the feasible solution in the convex domain.
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Keyword(in English) quadratic optimaization / inequality conditions / design of CNN
Paper # NLP95-57
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Conference Information
Committee NLP
Conference Date 1995/10/19(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Design Technique of Cellular Neural Networks : Optimaization methods constrained by inequality conditions
Sub Title (in English)
Keyword(1) quadratic optimaization
Keyword(2) inequality conditions
Keyword(3) design of CNN
1st Author's Name Koji NAKAI
1st Author's Affiliation Dept. of Electronic Control Engineering, Niihama Natioal College of Techology()
2nd Author's Name Akio USHIDA
2nd Author's Affiliation Dept. of Electrical and Electronic Engineering, Tokushima University
Date 1995/10/19
Paper # NLP95-57
Volume (vol) vol.95
Number (no) 296
Page pp.pp.-
#Pages 8
Date of Issue