A constructive algorithm for fuzzy neural networks

We propose a constructive method, inspired by Simpson’s Min-Max technique, for obtaining fuzzy neural networks. It adopts a cost function depending on a unique net parameter. This feature allows us to apply a simple unimodal search for determining this parameter and hence the architecture of the optimal net. The algorithm shows a good behavior with respect to other methods when applied to real classification problems. Due to the adopted fuzzy membership functions, it is particularly indicated when the classes are extremely overlapped (for instance, in the case of biological data). Some results at this regard are reported in the paper.