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

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.

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