Nonlinear H-infinity Control of Delayed Recurrent Neural Networks Influenced by Uncertain Noise

Volume 1, Issue 1, October 2016     |     PP. 1-24      |     PDF (584 K)    |     Pub. Date: October 16, 2016
DOI:    448 Downloads     5453 Views  

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 H-infinity optimal control is achieved for delayed recurrent neural networks with noise uncertainty. Our objective is to build globally stabilizing control laws to accomplish the input-to-state stability together with the optimality for delayed recurrent neural networks, and to attenuate noises to a predefined level with stability margins. The formulation of H-infinity control is developed by using Lyapunov technique and solving a Hamilton-Jacobi-Isaacs (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 H-infinity optimal control, noise attenuation, input-to-state stability, Lyapunov technique, Hamilton-Jacobi-Isaacs (HJI) equation.

Cite this paper
Ziqian Liu, Nonlinear H-infinity Control of Delayed Recurrent Neural Networks Influenced by Uncertain Noise , SCIREA Journal of Information Science and Systems Science. Volume 1, Issue 1, October 2016 | PP. 1-24.

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