Chaotic associative memory for sequential patterns

Abstract

We propose a chaotic associative memory for sequential patterns (CAMSP). The proposed CAMSP is based on a chaotic associative memory composed of chaotic neurons. In the conventional chaotic neural network, when a stored pattern is given to the network as an external input continuously, the input pattern is searched. The CAM makes use of this property in order to separate the superimposed patterns. In this research, the CAM is applied to associations for sequential patterns. The proposed model has the following features: 1) it can deal with associations for the sequential patterns; 2) it can realize associations by considering patterns' history; and 3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed model.