AN ALTERNATIVE APPROACH FOR GENERATION OF MEMBERSHIP FUNCTIONS AND FUZZY RULES BASED ON RADIAL AND CUBIC BASIS FUNCTION NETWORKS

The theoretically attractive fact that the radial basis function networks can be interpreted as fuzzy systems is of small importance for practical applications such as diagnosis and quality control with large numbers of inputs or hidden neurons, due to the lack of transparency of the resulting fuzzy systems. A novel method for the generation of fuzzy classification systems based on radial basis function networks with restricted Coulomb energy learning is presented. The neural network and the learning algorithm are modified for easy hardware implementation by introducing cubic basis functions. The proposed methods are rested with three application examples. The simulation results show the generation of compact, transparent fuzzy classification systems with good performance.