Evolutionary design of neurofuzzy networks for pattern classification

We consider a neural network based fuzzy system model whose basic processing unit consists of two types of generic logic (OR and AND) neurons. The net is structured into a multilayer topology and trained by a competitive learning algorithm, together with a genetic algorithm approach to select the most suitable triangular norms and co-norms that model the logic neurons. The main features of the system include: automatic rule generation and selection, learning capability, processing time independent of the input space partition, and automatic selection of the t-norms and s-norms that model the basic logic operators (OR, AND) encountered in the theory of fuzzy sets. Four benchmark problems are considered to compare the performance of the proposed method with those produced by alternative strategies