Inference systems by using ordinal sums and genetic algorithms

In this work a new approach developed by using ordinal sums to apply general t-norms to inference systems of different neuro-fuzzy systems is proposed. A genetic algorithm based strategic to search the best t-norms and/or t-conorms from data is adopted. By using the approach two known neuro-fuzzy systems, that are the fuzzy basis function network and the fuzzy relation neural network models are compared. Several experiments on synthetic and benchmark data using different parametric and non-parametric t-norms and t-conorms are made.