In this paper a fuzzy model of on experimental flexible arm is presented The identification of the fuzzy model parameters is performed on a combined associative+neuro-fuzzy network. Position and vibration measures have been taken on the real system to create the training patterns for the network, implementing the fuzzy model of the nonlinear system. The data acquisition has been performed by using a very low cost sensor equipment. A description of the combined network and the learning algorithms is provided. The identification results are shown and the response of the fuzzy model and of the real system to the same input are compared.
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