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.
A sparse Bayesian model for random weight fuzzy neural networks / Altilio, Rosa; Rosato, Antonello; Panella, Massimo. - (2018), pp. 1-7. (Intervento presentato al convegno IEEE International Conference on Fuzzy Systems tenutosi a Rio de Janeiro; Brazil) [10.1109/FUZZ-IEEE.2018.8491645].
A sparse Bayesian model for random weight fuzzy neural networks
Altilio, Rosa;Rosato, Antonello;Panella, Massimo
2018
Abstract
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.File | Dimensione | Formato | |
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