This paper deals with a new fuzzy adapting rate for a neural emulator of nonlinear systems with unknown dynamics. This method is based on an online intelligent adaptation by using a fuzzy supervisor. The satisfactory obtained simulation results are compared with those registered in the case of the classical choice of adapting rate and show very good emulation performances. An experimental validation of the proposed fuzzy adapting rate on a chemical reactor is also proposed to confirm the good performances in terms of speed of convergence and precision of representation.
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