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.