Knowledge processing system using chaotic associative memory

We propose a knowledge processing system using chaotic associative memory (KPCAM). The proposed KPCAM is based on a chaotic associative memory (CAM) 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 and to deal with many-to-many associations. In this research, the CAM is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: 1) it can deal with the knowledge which is represented in a form of semantic network; 2) it can deal with characteristics inheritance; and 3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.