Research on Properties of Max-Product Fuzzy Associative Memory Networks

In general, there is small perturbation between a pattern obtained by a certain acquisition way and the corresponding actual pattern in real word. Such small perturbation may cause disadvantage to several performance of a fuzzy neural network. Thus, a new concept is established in the paper that the robustness of a feed-forward fuzzy associative memory to perturbations of training pattern pair. Then for a Max-product-based fuzzy associative memory (Max-Product FAM), the investigation reveal that such robustness of the memory is good when the fuzzy Hebbian learning algorithm is used, however is poor when another learning algorithm is employed. Finally, an experiment is given to testify the theoretical conclusion and illustrate practical application of Max-product FAM.