In this paper a new structure of a recurrent neurofuzzy network is proposed. The network considers two cascade-interconnected Fuzzy Inference Systems (FISs), one recurrent and one static, that model the behaviour of a unknown dynamic system from input-output data. Each FIS's rule involves a linear system in a controllable canonical form. The training for the recurrent FIS is made by a gradient-based Real-Time Recurrent Learning Algorithm (RTRLA), while the training for the static FIS is based on a simple gradient method. The initial parameter conditions previous to training are obtained by extracting information from a static FISs trained with delayed input-output signals. To demonstrate its effectiveness, the identification of two non-linear dynamic systems is included. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Gonzalez-Olvera, M. A., & Tang, Y. (2006). A new recurrent neurofuzzy network for identification of dynamic systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 796–801). Springer Verlag. https://doi.org/10.1007/11760023_118
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