Observer-based finite-time bounded analysis for switched inertial recurrent neural networks under the PDT switching law

Zhengguo Huang, Jianwei Xia, Jing Wang, Jian Wang and Hao Shen

Physica A: Statistical Mechanics and its Applications, 2020, vol. 538, issue C

Abstract: In this note, the observer-based finite-time boundedness analysis issue for the switched inertial recurrent neural networks ( SIRNNs) is investigated deeply. The switching law, persistent dwell-time, with more generality and universality is employed. The first target is to develop a switched estimation system (SES) to obtain the states from the output of the researched open-loop SIRNNs. Thereafter, based on the before-mentioned SES, the resulting switched estimation error system (SEES) without the extrinsic disturbance, along with the closed-loop SIRNNs under state feedback controller are constructed. Furthermore, the sufficient conditions that the exponential stability for the SEES and the finite-time boundedness for the closed-loop SIRNNs are established simultaneously. The relevant estimator and controller gains are deduced by a straightforward decoupling manner. Ultimately, the feasibility of the method proposed is clarified and illustrated via a numerical example.

Keywords: Finite-time boundedness; Switched inertial recurrent neural networks; Persistent dwell-time; Observer-based controller (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:538:y:2020:i:c:s0378437119315389

DOI: 10.1016/j.physa.2019.122699

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