State Estimation for Standard Neural Network Models with Time-Varying Delays. (25th August 2022)

• Record Type: Journal Article
• Authors: Zhu, Jin; Li, Tai-Fang; Wang, Huanqing
• Other Names: Chen Chih-Chiang Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-08-25
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/4618101
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The paper deals with the issue of state estimation for standard neural network models with time-varying delays. A new augmented vector with the derivative of the state is introduced in the Lyapunov–Krasovskii functional. The state estimation criteria are obtained by constructing the suitable Lyapunov–Krasovskii functional; meanwhile, the observer gain and the controller gain are derived in terms of linear matrix inequality. The free matrix-based integral inequality is utilized to handle the integral terms, and the zero equation is added to the derivative of the Lyapunov–Krasovskii functional, which decreases the conservatism. The effectiveness and feasibility of the proposed methods are demonstrated by two numerical examples.