Abstract
Mamdani-type inference systems with trape- zoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or con- struction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard ''min'' t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the gen- eral parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of mod- eling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed.
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