A Novel Chaotic Neural Network for Automatic Material Ratio System

Aiming at improving automatic level of traditional material ratio control system, we propose a novel chaotic neural network (NCNN) for automatic material ratio control system. The NCNN controller has following properties: (1) separation of superimposed ratio patterns, (2) learning unknown ratio patterns successively. As for the first feature, we utilize the feature of chaotic neural network proposed by K.Aihara [1]. As for the first property, when a stored pattern is given to the network, the network searches around the input pattern by chaos, and it recalls the ratio patterns from superimposed patterns. As for the second one, when an unknown input pattern is given, a different response will be received. So it can distinguish unknown patterns from the known patterns and learn the unknown patterns successively. A series of computer simulations demonstrate the effectiveness and stability of the proposed method.

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