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.


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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