Non-normalised compensatory hybrid fuzzy neural networks

Abstract

Fuzzy neural networks have been shown to be superior to conventional multilayered backpropagation neural networks (BPNN). However, it is still an important problem to make fuzzy neural networks learn faster and to optimise membership functions of fuzzy rule based models to converge to a local minimum. Moreover, while learning faster and optimising, it is important to use less memory and to need less CPU time. In this paper, to overcome these problems, we propose non-normalised compensatory hybrid fuzzy neural networks (non-normalised CFBPNN) incorporating fuzzy c-means clustering as a fuzzy inference engine, fuzzy logic and backpropagation learning algorithms. The results have shown that the proposed algorithm overcomes these problems, and yields a very high performance. This algorithm was tested on the XOR problem, nonlinear function learning and pattern classification, and compared with normalised CFBPNN and BPNN to verify the algorithm.