Neuro-fuzzy systems derived from quasi-triangular norms

Most neuro-fuzzy systems proposed in the past decade employ "engineering implications" defined by a t-norm, e.g. the minimum or the product. We apply a new class of operators called quasi-triangular norms for the construction of neuro-fuzzy systems. These operators depend on a certain parameter ν and change their functional forms between a t-norm and a t-conorm. Consequently, the structure of neuro-fuzzy systems presented in the paper is determined in the process of learning. Learning procedures are derived and simulation examples are presented.