The paper presents the neuro-fuzzy network in application to the approximation of the static and dynamic functions. The network implements the Takagi-Sugeno inference rules. The learning algorithm is based on the hybrid approach, splitting the learning phase into two stages : the adaptation of the linear output weights using the SVD algorithm and the conventional steepest descent backpropagation rule in application to the adaptation of the nonlinear parameters of the membership functions. The new approach to the generation of the inference rules, based on the fuzzy self-organization is proposed and the algorithm of automatic determination of the number of these rules has been also implemented. The method has been applied for the off-line modelling of static nonlinear relations and on-line simulation of the dynamic systems.
Prof. ; elektrotechnika, teoria obwodów, sieci neuronowe, komputerowe metody analizy i projektowania obwodów ; Instytut Elektrotechniki Teoretycznej i Miernictwa Elektrycznego Politechniki Warszawskiej
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