摘要： 基于最大运算Max以及带参数ξ的t-模Tξ的模糊关系合成，提出一种参数化的广义模糊联想记忆网络Max-Tξ FAM及一种有效学习算法。由于Tξ中参数ξ的作用，在应用中Max-Tξ FAM有更大的适应性和灵活性。从理论上证明采用该学习算法时，对任意 ，Max-Tξ FAM对训练模式摄动的鲁棒性差。通过一个图像联想方面的实验检验了该结论的正确性。

关键词: 模糊神经网络, 模糊联想记忆网络, 学习算法, 鲁棒性, t-模

Abstract: Based on fuzzy composition of maximum operation and a t-norm Tξ with a parameter ξ, a parameterized general fuzzy associative memory network Max-Tξ FAM and its effective learning algorithm are presented. By adjusting parameter ξ, the Max-Tξ FAM has good adaptability and flexibility in practice. It is proved theoretically that, using the mentioned above learning algorithm, Max-Tξ holds weak robustness to perturbations of training pattern pairs for any [0.1]. Experiment about image association validates this conclusion.

Key words: fuzzy neural network, fuzzy associative memory network, learning algorithm, robustness, t-norm

中图分类号: 

• TP18