Comparison of Neuro-Fuzzy Structures for System Identification



In this paper an experimental comparison of neuro-fuzzy structures, namely linguistic and zero and first order Takagi-Sugeno, is developed. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase the structure of the model is obtained by subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. In the second phase, the model parameters are tuned via the training of a neural network. Furthermore, different fuzzy operators are compared, as well as regular and two-sided Gaussian functions.


system identification, neural networks, fuzzy systems, neuro-fuzzy networks, clustering


Neuro-Fuzzy Modelling


Control'2000, September 2000

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