Abstract
A neurofuzzy scheme is proposed to perform an online identification of nonlinear systems that can be represented by a transfer function with varying parameters. The parameter variation case due to one external variable is studied. The proposed scheme is composed of two blocks. The first one involves a fuzzy partition of the external variable universe of discourse. This partition is used to smoothly commute between several linear models. In the second block, a recurrent linear neuron with interpretable weights performs the identification of the models by means of supervised learning. The resulting identifier has two main advantages: interpretability, because the weights of the neuron can be assimilated to coefficients of transfer functions; and learning speed, due to the local behaviour imposed by the fuzzy partition. The proposed scheme tested on a real laboratory plant as an online identifier on an adaptive predictive control structure shows a good performance.
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