一类广义模糊Hopfield网络的稳定性

2009 International Conference on Apperceiving Computing and Intelligence Analysis Pub Date : 2009-12-28 DOI:10.1109/ICACIA.2009.5361139

Chunlin Wang, Fu Shi-lu, Dan Qi

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引用次数: 1

摘要

基于最大(V)和弱连续t模的模糊算子组合，提出了一种通用的动态模糊神经网络模型——模糊Hopfield网络。证明了该模型在Hamming距离下是全局稳定的，其平衡点(吸引子)是Lyapunov稳定的。最后，提出了一种简单高效的学习算法——最大权重矩阵学习算法。

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The stability of a class of generalized fuzzy Hopfield networks

A general dynamical fuzzy neural networks model—fuzzy Hopfield networks is developed, which is based on the fuzzy operator composition of max(V) and weakly continuous T-norms. It is shown that the model is global stable with the Hamming distance, and its equilibrium point (attractor) is Lyapunov stable. At last, a simple efficient learning algorithm —max-weight-matrix learning algorithm is proposed for it.

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2009 International Conference on Apperceiving Computing and Intelligence Analysis

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