Recall and separation ability of chaotic associative memory with variable scaling factor

In this paper, we propose a chaotic associative memory (CAM) with variable scaling factor which can separate superimposed patterns and can deal with one-to-many associations. In the proposed model, the appropriate parameters of chaotic neurons can be determined easily than in the original chaotic associative memory.

[1]  K. Aihara,et al.  Chaotic neural networks , 1990 .

[2]  E. Caianiello.  Outline of a theory of thought-processes and thinking machines. , 1961, Journal of theoretical biology.

[3]  J. Nagumo,et al.  On a response characteristic of a mathematical neuron model , 1972, Kybernetik.

[4]  Masafumi Hagiwara,et al.  Separation of superimposed pattern and many-to-many associations by chaotic neural networks , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).