Regular Papers

International Journal of Control, Automation and Systems 2021; 19(10): 3297-3308

Published online July 27, 2021

https://doi.org/10.1007/s12555-020-0474-x

© The International Journal of Control, Automation, and Systems

Receding Horizon Stability Analysis of Delayed Neural Networks with Randomly Occurring Uncertainties

Liankun Sun, Yanyu Wang*, and Wanru Wang

Tiangong University

Abstract

For delayed neural networks with randomly occurring uncertainties (ROU), this paper uses an improved integral inequality to optimize the stability of the receding horizon. The ROU follows some uncorrelated Bernoulli distribution white noise sequence, which it can enter the neural network in a free and random manner. By using a suitable lemma, the ROU problem added in this paper is transformed into a linear matrix inequality. Based on the auxiliary function-based integral inequality method, the new cross terms matrix of linear matrix inequality in the improved Lyapunov-Krasovskii functional is processed. Therefore, some new matrix variables containing more information are generated, so that the results have more degrees of freedom. This paper has obtained the new condition of the end-weighting matrix of the receding horizon cost function, thereby reducing its conservativeness and increasing its upper limit of delay. Finally, the superiority of the method has be illustrated by giving some simulation numerical examples.

Keywords Neural network, randomly occurring uncertainties, receding horizon stabilization, time delay.

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(10): 3297-3308

Published online October 1, 2021 https://doi.org/10.1007/s12555-020-0474-x

Copyright © The International Journal of Control, Automation, and Systems.

Receding Horizon Stability Analysis of Delayed Neural Networks with Randomly Occurring Uncertainties

Liankun Sun, Yanyu Wang*, and Wanru Wang

Tiangong University

Abstract

For delayed neural networks with randomly occurring uncertainties (ROU), this paper uses an improved integral inequality to optimize the stability of the receding horizon. The ROU follows some uncorrelated Bernoulli distribution white noise sequence, which it can enter the neural network in a free and random manner. By using a suitable lemma, the ROU problem added in this paper is transformed into a linear matrix inequality. Based on the auxiliary function-based integral inequality method, the new cross terms matrix of linear matrix inequality in the improved Lyapunov-Krasovskii functional is processed. Therefore, some new matrix variables containing more information are generated, so that the results have more degrees of freedom. This paper has obtained the new condition of the end-weighting matrix of the receding horizon cost function, thereby reducing its conservativeness and increasing its upper limit of delay. Finally, the superiority of the method has be illustrated by giving some simulation numerical examples.

Keywords: Neural network, randomly occurring uncertainties, receding horizon stabilization, time delay.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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