A sparse Bayesian model for random weight fuzzy neural networks

This paper introduces a sparse learning strategy that is suited for any fuzzy inference model, in particular to the Adaptive Neuro-Fuzzy Inference System, in order to optimize the generalization capability of the resulting model. This depends on two main issues: the estimate of numerical parameters of each fuzzy rule and the whole number of rules to be used. In this work, the former problem is solved by considering a random weight fuzzy neural network, where the fuzzy rule parameters of antecedents (i.e., membership functions) are randomly generated and the ones of rule consequents are estimated using a Regularized Least Squares algorithm. The second problem is solved by pruning the coefficients of fuzzy rules following a procedure based on sparse Bayesian learning theory. Experimental results on well-known datasets prove the effectiveness of the proposed approach.