Exponential stability of delayed neural networks with delayed sampled-data inputs: An extended bilateral looped functional approach |
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| Institution: | 1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China;2. School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404120, PR China;1. Department of Electrical Engineering, Shiraz University of Technology, Shiraz 71557-13876, Iran;2. Department of Electrical Engineering, University of Zanjan, Zanjan 45371-38791, Iran;3. Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou, Yunlin 640301, Taiwan;1. Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, PR China;2. Department of Mechanical Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, PR China;1. Digital Economy Research Institute of Hangzhou Dianzi University-Yongjia, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;3. Department of Electrical and Computer Engineering, COMSATS University Islamabad (Lahore Campus), Lahore 54000, Pakistan;1. Technological Research Institute (IRT) of the Railway Sector RAILENIUM, France;2. Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Valenciennes F-59313, France;3. INSA Hauts-de-France, Valenciennes F-59313, France |
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| Abstract: | In this paper, the exponential stability of delayed neural networks (DNNs) with delayed sampled-data inputs is investigated via extended bilateral looped functional approach. Firstly, a new extended bilateral looped functional is constructed, which is differentiable at sampling intervals and can relax the constraints on positive definiteness when compared to traditional functionals. Then, less conservative criteria for exponential stability of DNNs with delayed sampled-data inputs expressed through linear matrix inequalities (LMIs) are obtained. Furthermore, the results are extended to T–S fuzzy DNNs with delayed sampled-data inputs, where corresponding stability conditions are likewise derived. Finally, two simulation examples are given to illustrate the validity of the main results. |
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