Presentation | 1995/5/22 Some combinatorial optimization problems always solvable by Hopfield neural networks and their applications and limitations Satoshi Matsuda, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Many trials to solve the combinatorial optimization problems by Hopfield neural networks have been made extensively, however, it is, of course, not guaranteed to get always optimal solutions. Then it is interesting of what kind of combinatorial optimization problems the optimal solutions can always be obtained by Hopfield neural networks. In this paper, by the theory of the asymptotical stability and unstability of solutions and nonsolutions made by the author, it is shown that the optimal solutions of n-rook problems, which have a similarity to crossbar switching and a practical significance, can always be obtained by Hopfield neural networks. But it is also shown that optimal solutions of "inverse n-rook problem", which is more precise model of crossbar, can not always be obtained. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Hopfield neural network / n-rook problem / crossbar switching |
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Committee | NC |
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Conference Date | 1995/5/22(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Some combinatorial optimization problems always solvable by Hopfield neural networks and their applications and limitations |
Sub Title (in English) | |
Keyword(1) | Hopfield neural network |
Keyword(2) | n-rook problem |
Keyword(3) | crossbar switching |
1st Author's Name | Satoshi Matsuda |
1st Author's Affiliation | Computer and Communication Research Center, Tokyo Electric Power Company() |
Date | 1995/5/22 |
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Volume (vol) | vol.95 |
Number (no) | 57 |
Page | pp.pp.- |
#Pages | 8 |
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