Max-min encoding learning algorithm for fuzzy max-multiplication associative memory networks

This paper proposes a kind of algorithm, called max-min encoding learning algorithm, for fuzzy max-multiplication (in short FMM) associative memory networks. The new method can store all auto-associative memory samples. Based on the max-min encoding, a kind of gradient descent learning method is presented to be identified as the connection weight for FMM hetero-associative memory networks. The simulation shows the effectiveness of the method