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A new robust training law for dynamic neural networks with external disturbance: an LMI approach. (English) Zbl 1206.93101

Summary: A new robust training law, which is called an Input/Output-to-State Stable Training Law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on Linear Matrix Inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

MSC:

93D25 Input-output approaches in control theory
92B20 Neural networks for/in biological studies, artificial life and related topics
93C73 Perturbations in control/observation systems
93D20 Asymptotic stability in control theory
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