Presentation 1997/2/6
Hysteresis Quantizer and an Associative Memory
Kenya JIN'NO, Mamoru TANAKA,
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Abstract(in English) We have proposed a si1nple hysteresis network (ab. SHN) wThose connection coefficents have uniform value in our previous works. In this report, we propose a generalized hysteresis network (ab. GSHN) whose connection coeeficients are given by the correlation matrix of a bipolar vector We can clarify that the attractors are controled by the self feedback parameter and the input. We forcus on the case where the all attrctors are stable equilibria. We classify the equilibra and clarify the number of attractors and their domain of attraction. Based on the result, we consider the network whose connection coefficients don't have restriction such as the GSHN. And, we introduce the hysteresis quanitzer which is an application.
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Keyword(in English) hysteresis / neural networks / associative memory / hamming distance, quantizer
Paper # NLP96-127,NC96-81
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Committee NC
Conference Date 1997/2/6(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hysteresis Quantizer and an Associative Memory
Sub Title (in English)
Keyword(1) hysteresis
Keyword(2) neural networks
Keyword(3) associative memory
Keyword(4) hamming distance, quantizer
1st Author's Name Kenya JIN'NO
1st Author's Affiliation Department of Electrical E1ectronics Engin()
2nd Author's Name Mamoru TANAKA
2nd Author's Affiliation eering, Sophia University
Date 1997/2/6
Paper # NLP96-127,NC96-81
Volume (vol) vol.96
Number (no) 511
Page pp.pp.-
#Pages 6
Date of Issue