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多模式對(duì)連接權(quán)矩陣的一種神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法

楊群生,  余英林

楊群生, 余英林. 多模式對(duì)連接權(quán)矩陣的一種神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法[J]. 電子與信息學(xué)報(bào), 2001, 23(3): 280-285.
引用本文: 楊群生, 余英林. 多模式對(duì)連接權(quán)矩陣的一種神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法[J]. 電子與信息學(xué)報(bào), 2001, 23(3): 280-285.
Yang Qunsheng, Yu Yinglin . A Kind of Neural Networks Learning Algorithms for Multiple-Pattern Pairs Weighted Matrix[J]. Journal of Electronics & Information Technology, 2001, 23(3): 280-285.
Citation: Yang Qunsheng, Yu Yinglin . A Kind of Neural Networks Learning Algorithms for Multiple-Pattern Pairs Weighted Matrix[J]. Journal of Electronics & Information Technology, 2001, 23(3): 280-285.

多模式對(duì)連接權(quán)矩陣的一種神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法

A Kind of Neural Networks Learning Algorithms for Multiple-Pattern Pairs Weighted Matrix

  • 摘要: 該文提出多模式對(duì)連接權(quán)矩陣的一種神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法,并給出了嚴(yán)格的理論證明。該算法能夠?qū)⒍鄠€(gè)模糊模式對(duì)可靠地編碼存儲(chǔ)到盡可能少的連接權(quán)矩陣中,從而大大地減少存儲(chǔ)空間,而且容易實(shí)現(xiàn),并舉例驗(yàn)證了它的有效性。
    Abstract: A kind of neural networks learning algorithms for multiple-pattern pairs weighted matrix of fuzzy associative memories(FAMs) and its strict theoretic proofs are presented in this paper. Multiple fuzzy pattern pairs can be encoded to store in FAM connection weight matrixes as few as possible by the algorithm, so it can cut down storage space greatly and this algorithm can easily be implimented. Its effectiveness is testified by an example.
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  • B. Kosko, Fuzzy Associative Memory Systems, Fuzzy Expert Systems Reading, MA, Addison-Weley, 1987.[2]B. Kosko, Neural Networks and Fuzzy Systems, New Jersy, Prentic-Hall, Inc., 1992, 299-339.[3]J.b. Fan, F. Jin F, Y. Shi, A class of fuzzy associative memories, Chinese Journal of Electronics, 1993, 2(1): 26-30.[4]范俊波,靳蕃,史燕,模糊聯(lián)想記憶網(wǎng)絡(luò)的一種有效學(xué)習(xí)算法,電子學(xué)報(bào),1996,24(1),112-114.[5]W. Pedrycz, Processing in relational structures, Fuzzy relational equations.[J]. Fuzzy Sets and Systems.1991,40:77-[6]A. Blanco, M. Delgado, I. Requena, Identification of relational equations by fuzzy neural networks.[J]. Fuzzy Sets and Systems.1995,71:215-[7]A. Blanco, M. Delgado, I. Requena, Improved fuzzy neural networks for solving relational equations.[J]. Fuzzy Sets and Systems.1995,72:311-[8]劉普寅,基于一類模運(yùn)算的模糊神經(jīng)網(wǎng)絡(luò)的模糊聯(lián)想記憶,系統(tǒng)工程與電子技術(shù),1997,11,54-58.[9]劉普寅,馬超群,張漢江,一類廣義模糊雙向聯(lián)想記憶的穩(wěn)定性,模糊系統(tǒng)與數(shù)學(xué),1998,12(4),40-46.[10]肖平,最大-乘積型模糊聯(lián)想記憶網(wǎng)絡(luò)的最大最小編碼學(xué)習(xí)算法,通信學(xué)報(bào),1999,20(1),7-22.[11]F. L. Chung, T. Lee, On fuzzy associative memory with multiple-rule storage capacity, IEEE Trans.[J]. on Fuzzy Systems.1996,4:375-[12]李曉忠,汪培莊,羅承忠,模糊神經(jīng)網(wǎng)絡(luò),貴陽,貴州科技出版社,1994,211-232.
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出版歷程
  • 收稿日期:  1999-04-29
  • 修回日期:  1999-10-15
  • 刊出日期:  2001-03-19

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