计算机工程与应用Issue(5)：96-100,107,6.DOI:10.3778/j.issn.1002-8331.1204-0707
模糊联想记忆网络的全局鲁棒性研究--基于爱因斯坦t-模
Analysis of robustness of fuzzy associative memory based on Einstain’s t-norm
高翔 1马亨冰2
作者信息
- 1. 福州大学 数学与计算机科学学院，福州 350108
- 2. 福建省经济中心，福州 350003
- 折叠
摘要
Abstract
The paper analyses the robustness of learning algorithm for fuzzy associative memory based on Einstain’s t-norm by using the properties of fuzzy bidirectional associative memories based on triangular norms and the overall situation robustness of fuzzy bidirectional associative memories. The conclusion that the learning algorithm can keep good overall robustness when the perturbations are positive is proved in theory and verified by experiment in this paper. And that the learning algorithm doesn’t satisfy overall situation robustness when the noise contains negative value is proved by experi-ment. What is more, the relation between the maximum of perturbations of training patterns and the maximum of perturba-tions of the output is also analyzed and the relation curve is gotten.关键词
爱因斯坦t-模/模糊联想记忆网络/学习算法/全局鲁棒性Key words
Einstain t-norm/fuzzy bidirectional associative memory/learning algorithm/overall situation robustness分类
信息技术与安全科学引用本文复制引用
高翔,马亨冰..模糊联想记忆网络的全局鲁棒性研究--基于爱因斯坦t-模[J].计算机工程与应用,2014,(5):96-100,107,6.
