Rough knowledge-based network, fuzziness and classification

Abstract

A method of integrating rough sets and fuzzy multilayer perceptron (MLP) for designing a knowledge-based network for pattern recognition problems is described. Rough set theory is used to extract crude knowledge from the input domain in the form of rules. The syntax of these rules automatically determines the optimal number of hidden nodes while the dependency factors are used in the initial weight encoding. Results on classification of speech data demonstrate the superiority of the system over the fuzzy and conventional versions of the MLP.