Stability analysis of dynamic neural control

Abstract In this paper, the authors summarize their research related to dynamic neural control. In particular, results on nonlinear system identification, nonlinear trajectory tracking, and input-to-state stability (ISS) of dynamic neural networks are presented. The main analysis tool utilized is the Lyapunov approach. References for the detailed demonstrations are given. We illustrate the applicability of the results by means of examples.

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