Fuzzy neural nets with non-symmetric π membership functions and applications in signal processing and image analysis

Abstract

Interpolation, estimation and classification, widely used in signal processing and image analysis, can be considered as problems of optimization. Different systems could be used; some are based on known numerical data, and the others, on expert rules. In general, they have difficulty to integrate both the knowledge of experts and that implied in known numerical training samples. In the present paper, we propose to use neural fuzzy systems with non-symmetric π membership functions. A new global optimization criterion and the learning algorithm are also presented. Experimental results of applications to interpolation, estimation and classification problems are reported. The comparison with other methods shows a better behavior of such systems. Non-symmetric π membership function gives a more general model of fuzzy rules, improving the precision of neural fuzzy system and assuring a good convergence in learning. The neural fuzzy system using non-symmetric π membership functions allows integrating both the knowledge of experts and that implied in numerical training samples.