Nonlinear dynamic system identification and control via constructivism inspired neural network

This paper deals with a novel idea of identification of nonlinear dynamic systems via a constructivism inspired neural network. The proposed network is known as growing multi-experts network (GMN). In GMN, the problem space is decomposed into overlapping regions by expertise domain and local expert models are graded according to their expertise level. The network output is computed by the smooth combination of local linear models. In order to avoid over-fitting problem, GMN deploys a redundant experts removal algorithm to remove the redundant local experts from the network. In addition, growing neural gas (GNG) algorithm is used to generate an induced Delaunay triangulation that is highly desired for optimal function approximation. A variety of examples are taken from literature to establish the efficacy of GMN. Discrete time nonlinear dynamic system modeling and water bath temperature control have been found to give excellent results via this novel neural network.

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