Regular Papers

International Journal of Control, Automation and Systems 2017; 15(5): 2385-2394

Published online July 20, 2017

https://doi.org/10.1007/s12555-016-0315-0

© The International Journal of Control, Automation, and Systems

Analysis on Robust Passivity of Uncertain Neural Networks with Time-Varying Delays via Free-Matrix-Based Integral Inequality

Shen-Ping Xiao*, Hong-Hai Lian, Hong-Bing Zeng, Gang Chen, and Wei-Hua Zheng

Hunan University of Technology

Abstract

This paper investigates the robust delay-dependent passivity problem of neural networks (NNs) with time-varying delays and parameter uncertainties. A suitable augmented Lyapunov-Krasovskii functional (LKF) with triple integral term, which takes full use of the neuron activation function conditions and the information of time-delay in integral term, is constructed. Furthermore, by utilizing integral inequality proposed recently and the combining reciprocally convex method to estimate the derivative of the LKF, some less conservative robust passivity conditions are derived in terms of LMI. The superiority of presented approaches are demonstrated via two classic numerical examples"

Keywords Integral inequality, neural networks, parameter uncertainties, passivity, time-varying delays.

Article

Regular Papers

International Journal of Control, Automation and Systems 2017; 15(5): 2385-2394

Published online October 1, 2017 https://doi.org/10.1007/s12555-016-0315-0

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

Analysis on Robust Passivity of Uncertain Neural Networks with Time-Varying Delays via Free-Matrix-Based Integral Inequality

Shen-Ping Xiao*, Hong-Hai Lian, Hong-Bing Zeng, Gang Chen, and Wei-Hua Zheng

Hunan University of Technology

Abstract

This paper investigates the robust delay-dependent passivity problem of neural networks (NNs) with time-varying delays and parameter uncertainties. A suitable augmented Lyapunov-Krasovskii functional (LKF) with triple integral term, which takes full use of the neuron activation function conditions and the information of time-delay in integral term, is constructed. Furthermore, by utilizing integral inequality proposed recently and the combining reciprocally convex method to estimate the derivative of the LKF, some less conservative robust passivity conditions are derived in terms of LMI. The superiority of presented approaches are demonstrated via two classic numerical examples"

Keywords: Integral inequality, neural networks, parameter uncertainties, passivity, time-varying delays.

IJCAS
September 2024

Vol. 22, No. 9, pp. 2673~2953

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