Presentation | 1996/7/24 A Study of Back Propagation Learning with Periodic Activation Function Takashi Okada, Masahiro Nakagawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this report, we shall propose a back propagation learning model with a periodic activation function, and investigate the learning ability. The present neural network model is found to avoid an unfavorable trapping at local minima and remarkably assure the convergence to the global minimum as a periodic activation function, which promotes considerably the learning ability. In practice we shall investigate a 3-layered neural network model applied to the XOR problem. From the present simulation results, it is found that the learning speed can be relatively improved in comparison with the conventional back propagation learning model. In addition, it is also found that a chaotic noise in the learning scheme results in an advantage to the learning speed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Periodic Activation Function / Chaos / Back Propagation / Learning Speed |
Paper # | NC96-23 |
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Conference Information | |
Committee | NC |
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Conference Date | 1996/7/24(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 | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Study of Back Propagation Learning with Periodic Activation Function |
Sub Title (in English) | |
Keyword(1) | Periodic Activation Function |
Keyword(2) | Chaos |
Keyword(3) | Back Propagation |
Keyword(4) | Learning Speed |
1st Author's Name | Takashi Okada |
1st Author's Affiliation | Department of Electrical Engineering, Faculty of Engineering,Nagaoka University ofTechnohogy() |
2nd Author's Name | Masahiro Nakagawa |
2nd Author's Affiliation | Department of Electrical Engineering, Faculty of Engineering,Nagaoka University ofTechnohogy |
Date | 1996/7/24 |
Paper # | NC96-23 |
Volume (vol) | vol.96 |
Number (no) | 178 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |