Some new results on system identification with dynamic neural networks.
Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L(infinity) sense and robust to any bounded uncertainties.
Yu
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W
Departamento de Control Automatico, CINVESTAV-IPN, Mexico D.F., 07360, Mexico. yuw@ctrl.cinvestav.mx
Li
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X