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.
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Keyword(in English) Complex-valued Neural Networks / Multistate Associative Memory / Gray-Level Image Reconstruction
Paper # NLP2007-144
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Conference Information
Committee NLP
Conference Date 2008/1/25(1days)
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Registration To Nonlinear Problems (NLP)
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
Date of Issue