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

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2 Author(s)
P. Mastorocostas ; Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Greece ; J. Theocharis

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

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Fuzzy Systems, 2001. The 10th IEEE International Conference on  (Volume:1 )

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