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Zbl 1206.93101
Ahn, Choon Ki
A new robust training law for dynamic neural networks with external disturbance: an LMI approach. (English)
[J] Discrete Dyn. Nat. Soc. 2010, Article ID 415895, 14 p. (2010). ISSN 1026-0226; ISSN 1607-887X/e

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 2000:: *93D25 Input-output approaches to stability of control systems
92B20 General theory of neural networks
93C73 Perturbations in control systems
93D20 Asymptotic stability of control systems

Keywords: robust training; input/output-to-state stable training; dynamic neural networks; external disturbance; exponential stability; linear matrix inequality

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