Fast self-learning multivariable fuzzy controllers constructed from a modified CPN network

Although previous papers have focused primarily on the functional mapping between an approximate reasoning algorithm and the neural network approach, here we are concerned mainly with structural mapping between those two paradigms. Our objective is to deal with three different but correlated issues, rule-base acquisition, computational representation, and reasoning, under a unified framework of CPN-based neural networks. In particular, we introduce a simple and systematic scheme capable of self-organizing and self-learning the required control knowledge in a real-time manner for a multivariable process. The starting point of the approach is to map structurally a simplified fuzzy control algorithm (SFCA) developed by the authors into a counterpropagation network (CPN) in such a way that the control knowledge is explicitly represented in the form of connection weights of the nets. Then, by extending original training algorithms at both the Kohonen and Grossberg layers into highly self-organized and complete...