Presentation | 2008-02-01 Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Function Gouhei TANAKA, Kazuyuki AIHARA, |
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Abstract(in English) | A complex-valued neural network can be used for multistate associative memory by quantizing a neuronal state into a multivalued state with an appropriate threshold function. The complex-signum function used in conventional multistate associative memory models is a transformation including a multilevel signum function in essence. In the present report, we propose a complex-valued threshold function based on nonlinear multilevel functions and show the recall performance of the multistate associative memory based on the proposed method. Numerical experiments clarify how the recall capability is influenced by the parameter controlling the nonlinearity of the multilevel function, the number of the stored patterns, and the number of quantized states. We also demonstrate gray-level image reconstruction with the proposed method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Complex-valued Neural Networks / Multistate Associative Memory / Gray-Level Image Reconstruction |
Paper # | NLP2007-144 |
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Committee | NLP |
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Conference Date | 2008/1/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Function |
Sub Title (in English) | |
Keyword(1) | Complex-valued Neural Networks |
Keyword(2) | Multistate Associative Memory |
Keyword(3) | Gray-Level Image Reconstruction |
1st Author's Name | Gouhei TANAKA |
1st Author's Affiliation | Institute of Industrial Science, University of Tokyo() |
2nd Author's Name | Kazuyuki AIHARA |
2nd Author's Affiliation | Institute of Industrial Science, University of Tokyo |
Date | 2008-02-01 |
Paper # | NLP2007-144 |
Volume (vol) | vol.107 |
Number (no) | 478 |
Page | pp.pp.- |
#Pages | 6 |
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