D-OLS: an orthogonal least squares method for dynamic fuzzy models

This paper presents an orthogonal least squares (OLS) based modeling method, named dynamic OLS (D-OLS), for generating recurrent fuzzy models. A dynamic-neuron based fuzzy neural network is proposed, comprising generalized Takagi-Sugeno-Kang fuzzy rules, whose consequent parts consist of dynamic neurons with local output feedback. From an arbitrarily large set of candidate dynamic neurons, the D-OLS method selects automatically the most important ones. Thus, each fuzzy rule of the resulting model contains a different number and kind of dynamic neurons. In the simulation results, the effectiveness of the suggested method as well as the advantages of the resulting dynamic model are demonstrated.