Dynamic associative memory using chaotic neural networks

In this paper, we propose a multimodule chaotic associative memory (MCAM) that uses chaotic neural networks. In this method, the chaotic associative memories are connected to each other. If MCAM can not obtain enough information of a target, MCAM shows a behavior that looks like human “perplexity”, where MCAM succeeds in one-to-many associations. And when MCAM obtains enough information to recognize a target, MCAM converges to a stable state. Although the structure of MCAM is simple, MCAM realizes one-to-many association by using chaotic dynamics