Chaotic multidirectional associative memory
Yuko Osana, Motonobu Hattori, Masafumi Hagiwara · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
A chaotic multidirectional associative memory (CMAM) is proposed and simulated. It can deal with many-to-many associations and the structure is very simple. Furthermore, similarity to a psychological fact (priming effect) is observed in the association of the CMAM. In order to enable many-to-many associations, the CMAM memorizes each training data together with its own contextual information and employs chaotic neurons. Since the chaotic neurons change their states by chaos, many-to-many associations can be realized in the CMAM.