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Author(s)
Ziqian Liu, Department of Engineering, State University of New York Maritime College, 6 Pennyfield Avenue, Throggs Neck, NY 10465, USA
Abstract
This paper presents a theoretical design of how a nonlinear Hinfinity optimal control is achieved for delayed recurrent neural networks with noise uncertainty. Our objective is to build globally stabilizing control laws to accomplish the inputtostate stability together with the optimality for delayed recurrent neural networks, and to attenuate noises to a predefined level with stability margins. The formulation of Hinfinity control is developed by using Lyapunov technique and solving a HamiltonJacobiIsaacs (HJI) equation indirectly. To illustrate the analytical results, three numerical examples are given to demonstrate the effectiveness of the proposed approach.
Keywords
Delayed recurrent neural networks, nonlinear Hinfinity optimal control, noise attenuation, inputtostate stability, Lyapunov technique, HamiltonJacobiIsaacs (HJI) equation.
Cite this paper
Ziqian Liu,
Nonlinear Hinfinity Control of Delayed Recurrent Neural Networks Influenced by Uncertain Noise, SCIREA Journal of Information Science and Systems Science. Vol.
1
, No.
1
,
2016
, pp.
1

24
.
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