Nonlinear dynamic system identification and control via self-regulating modular neural network - Kiong, L.C.; Rajeswari, M.; Rao, M.V.C. (2003)
An endeavor is made in this paper to describe a self-regulating constructive multi-model neural network called Self-regulating Growing Multi-Experts Network (SGMN) that can approximate an unknown nonlinear function from observed input-output training data. The proposed network is devised to overcome the redundancy problems of Gaussian neural networks that use square mesh partition method. In the SGMN, the problem space is decomposed into overlapping regions by expertise domain and the local expert models are graded according to their expertise level. The network output is computed by a smooth combination of local polynomial models. In order to avoid an over-fitting problem, the SGMN deploys a Redundant Experts Removal Algorithm to remove the redundant local experts from the network. In addition, the Fully Self-Organized Simplified Adaptive Resonance Theory (FOSART) is modified and adopted to generate an induced Delaunay triangulation that is highly desired for optimal function approximation. Self-adaptive learning rates Gradient Descent learning rules are employed in a supervised learning phase. A parametric control at epoch terminations and performance based on local incremental experts insertions are incorporated. A variety of examples is solved from literature to establish the efficacy of SGMN. Discrete time nonlinear dynamic system modeling and water bath temperature control have been found to give excellent results via this novel neural network.