| 摘要: |
| 基于Ｋｏｈｏｎｅｎ的广义逆联想存储模型ＧＩＡＭ（ｇｅｎｅｒａｌｉｚｅｄｉｎｖｅｒｓｅａｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）和Ｍｕｒａｋａｍｉ的最小平方联想存储ＬＳＡＭ（ｌｅａｓｔｓｑｕａｒｅｓａｓｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）原理，本文提出了一个指数型联想存储器．该模型的存储性能经计算机模拟证实，远远优于ＧＩＡＭ和ＬＳＡＭ，通过适当地调节参数，几乎可达到完全的联想．对输入噪声方差，无需先验假设，同时还实现了一定程度的非线性映射特性. |
| 关键词: 联想存储 神经网络 广义逆 指数 最小平方联想 非线性映射 |
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| 基金项目:本文研究得到国家基础研究“攀登计划”基金,江苏省自然科学基金资助. |
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| EXPONENTIAL ASSOCIATIVE MEMORY MODELBASED ON GENERALIZED INVERSE |
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CHEN Songcan,GAO Hang,ZHU Wujia
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| Abstract: |
| Based on Kohonen'S GIAM(generalized inverse associative memory)and Murakami'S LSAM(1east squares associative memory)principles, an exponential associa-tive memory is presented in this paper．The computer simulations have shown that the associative performance of the proposed model is superior to those of GIAM and LSAM，and its recall for stored data is almost perfect only via adjusting its parameter．The model does not require a prior assumption to noise variances and realizes nonlinear mapping between inputs and outputs to some extent． |
| Key words: Associative memory neural networks generalized inverse exponents lease squares association nonlinear mapping |
