| 摘要: |
| 多向联想记忆MDAM(multidirectional associative memory)模型是Kosko双向联想记忆模型BAM(bidirectional associative memory)的一个直接推广,它可应用于数据融合及维数分裂,使模型能处理大维数输入问题.目前所提出的若干种多向模型均局限于二值输入/输出模式对,但如在图象处理等的实际应用中,所处理的模式均是多值的.本文的目的就是提出一个多值指数式多向联想记忆模型MVeMDAM(multivalued exponential multidi |
| 关键词: (多向)联想记忆,多值,稳定性,指数,神经网络. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金资助. |
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| Multivalued Exponential Multidirectional Associative Memory |
|
CHEN Song-can,GAO Hang
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| Abstract: |
| MDAM (multidirectional associative memory) is a direct extension of Kosko BAM (bidirectional associative memory). It can be applied in data fusion and splitting larger dimensional input patterns to ease some problems to be solved. At present, the existing multidirectional models only dealt with binary input-output patterns or data. However, some patterns in such applications as image processing and pattern recognition are represented in a multivalued mode. Therefore, the above models have some processing difficulties. The purpose of this paper is to present a MVeMDAM (multi-valued exponential associative memory) to partially solve the difficulties. In this paper, the stability of the MVeMDAM is proven in synchronous and asynchronous update modes for neuron states, which enables the MVeMDAM to ensure all the training pattern sets to become the stable states of the system. Finally, the computer simulation results confirm feasibility of the proposed model. |
| Key words: (Multidirectional) associative memory, multivalued, stability, exponent, neural networks. |
