Characteristics of associative chaotic neural networks with weighted pattern storage-a pattern is stored stronger than others
Masaharu Adachi, Kazuyuki Aihara · 2003
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.