Robust stabilising controller synthesis for discrete-time recurrent neural networks via state feedback
by Jianhai Zhang, Huaixiang Zhang, Guojun Dai, Senlin Zhang, Meiqin Liu
International Journal of Modelling, Identification and Control (IJMIC), Vol. 11, No. 1/2, 2010

Abstract: This paper addresses the stabilisation problem of discrete-time recurrent neural networks (RNNs) containing norm-bounded uncertainties. A novel neural network model, named standard neural network model (SNNM), is used to provide a general framework for robust stabilising controller synthesis of RNNs. Most of the existing RNNs can be transformed into SNNM to be synthesised in a unified way. Applying the Lyapunov stability theory and the S-procedure technique, state feedback controllers are designed to guarantee the global asymptotical stability of closed-loop dynamic discrete-time systems. The controller gains are obtained by solving a set of linear matrix inequalities. Examples are given to illustrate the transformation procedure and the effectiveness of the proposed design technique.

Online publication date: Mon, 20-Sep-2010

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