| Presentation | 2002/3/13 H_∞- Learning : Local Optimization Approach Kiyoshi NISIYAMA, Kiyohiko SUZUKI, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Backpropagation (BP) method is widely known as a learning algorithm of layered neural networks. However, the learning rate is too late, and it is strongly affected by the initial values of weight coefficients and thresholds. In this paper, H_∞- learning of layered neural networks is proposed, and a new learning algorithm, called the l-EHF algorithm, is derived, comparing with the backpropagation (BP) and the extended Kalman filter (EKF) learning algorithms. The robustness of H_∞-learning to variances in the initial weights and thresholds is verified by computer simulations. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | learning algorithm / H_∞ theory / neural network / robust estimation / back-propagation / Kalman filter |
| Paper # | NC2001-224 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2002/3/13(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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) | H_∞- Learning : Local Optimization Approach |
| Sub Title (in English) | |
| Keyword(1) | learning algorithm |
| Keyword(2) | H_∞ theory |
| Keyword(3) | neural network |
| Keyword(4) | robust estimation |
| Keyword(5) | back-propagation |
| Keyword(6) | Kalman filter |
| 1st Author's Name | Kiyoshi NISIYAMA |
| 1st Author's Affiliation | Department of Computer and Information Science, Iwate University() |
| 2nd Author's Name | Kiyohiko SUZUKI |
| 2nd Author's Affiliation | Department of Computer and Information Science, Iwate University |
| Date | 2002/3/13 |
| Paper # | NC2001-224 |
| Volume (vol) | vol.101 |
| Number (no) | 737 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |