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Identification of nonlinear dynamical systems using multilayered neural networks. (English) Zbl 0879.93010

The paper deals with the open-loop identification of four classes of discrete-time, multi-input multi-output, nonlinear systems. The structure of the neural-net identifier is derived using a passivity approach, and the weight tuning algorithm relies on a slightly modified delta rule. It is shown that the persistency-of-excitation condition guarantees the convergence of the identification error and the boundedness of all the signals in the system. The certainty equivalence assumption is not used.

MSC:

93B30 System identification
92B20 Neural networks for/in biological studies, artificial life and related topics
93C10 Nonlinear systems in control theory
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