Backpropagation and genetic algorithms for training fuzzy neural nets

This paper concerns combined backpropagation and genetic training of fuzzy neural nets whose weights and signals are given as real or triangular fuzzy numbers. The proposed fuzzy neural network with backpropagation and genetic-based learning system is used on problems which map a fuzzy or real input to a fuzzy or real output based on interval arithmetic operations. Experimental results demonstrating characteristics of various nonlinear mappings are discussed.