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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yong Li Zheng Tang GuangPu Xia Rong Long Wang Xinshun Xu |
| Copyright Year | 2004 |
| Description | Author affiliation: Fac. of Eng., Toyama Univ., Japan (Yong Li; Zheng Tang; GuangPu Xia) |
| Abstract | In this paper, a fast and reliable approach to the traveling salesman problem (TSP) using the positively self-feedbacked Hopfield neural networks is proposed. The Hopfield neural networks with positive self-feedbacks and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural networks with positive self-feedbacks that the emergent collective properties of the original Hopfield neural networks also are present in this network. The network is applied to the TSP and results of computer simulations are presented and used to illustrate the computation power of the networks. The simulation results show that the Hopfield neural networks with positive self-feedbacks has a rate of success higher than the original Hopfield neural networks for solving the TSP, and converges faster to stable solution than the original Hopfield neural networks does. |
| Starting Page | 999 |
| Ending Page | 1004 |
| File Size | 887668 |
| Page Count | 6 |
| File Format | |
| ISBN | 4907764227 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-04 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | SICE |
| Subject Keyword | Hopfield neural networks Neurons Computer networks Reliability engineering Traveling salesman problems Computational modeling Optimization methods Neural networks Computer simulation Associative memory |
| Content Type | Text |
| Resource Type | Article |
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