| Presentation | 1997/9/19 Learning algorithm for Chaotic Neural Networks which Solve Optimization Problems Isao Tokuda, Kazuyuki Aihara, Ryuji Tokunaga, Tomomasa Nagashima, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Learning algorithm is introduced for chaotic neural networks which solve combinatorial optimization problems. The learning algorithm works as a chaotic simulated annealing (CSA) algorithm which controls the "chaotic search" dynamics to gradually converge to possible optimum or near-optimum solution of the problem. Using 5 and 20-city traveling salesman problems (TSP), efficiency of the learning algorithm is shown. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | NLP97-83 |
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| Conference Information | |
| Committee | NLP |
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| Conference Date | 1997/9/19(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Nonlinear Problems (NLP) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning algorithm for Chaotic Neural Networks which Solve Optimization Problems |
| Sub Title (in English) | |
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| 1st Author's Name | Isao Tokuda |
| 1st Author's Affiliation | Department of Computer Science and Systems Engineering, Muroran Institute of Technology() |
| 2nd Author's Name | Kazuyuki Aihara |
| 2nd Author's Affiliation | Department of Mathematical Engineering and Information Physics, Faculty of Engineering, University of Tokyo |
| 3rd Author's Name | Ryuji Tokunaga |
| 3rd Author's Affiliation | Institute of Information Sciences and Electronics, University of Tsukuba |
| 4th Author's Name | Tomomasa Nagashima |
| 4th Author's Affiliation | Department of Computer Science and Systems Engineering, Muroran Institute of Technology |
| Date | 1997/9/19 |
| Paper # | NLP97-83 |
| Volume (vol) | vol.97 |
| Number (no) | 258 |
| Page | pp.pp.- |
| #Pages | 5 |
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