Characteristics of associative chaotic neural networks with weighted pattern storage-a pattern is stored stronger than others

Associative chaotic neural networks with weighted pattern storage are studied. Values of the synaptic weights of conventional associative neural networks are determined by an auto-associative matrix. On the other hand, in this paper, we use a weighted auto-associative matrix in order to store a pattern that is stronger than the other stored patterns. Retrieval characteristics and dynamical properties of associative chaotic neural networks with this weighted auto-associative matrix are numerically analysed. As a result, the network retrieves the strongly stored pattern more frequently than other stored patterns, even in the case where the dynamics of the network is chaotic.