Parallel neurofuzzy learning and construction algorithm
Chris J. Harris, Xia Hong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
This paper establishes a connection between a neurofuzzy network model with the Mixture of Experts Network MEN modeling approach. Based on this connection, a new neurofuzzy MEN construction algorithm is proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The new construction algorithm is based on a new parallel learning method in which each model rule is trained independently, in which the parameter convergence property of the new learning method is established. By using the expert selective criterion of the MEN model output sensitivity to each expert, each rule can be selected to be trained or inhibited. The construction method is effective in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector with the additional computational advantage of parallel processing. The proposed algorithm is analyzed for effectiveness followed by a numerical example to illustrate the efficacy for some difficult data based modeling problem.