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Licensed Unlicensed Requires Authentication Published by De Gruyter October 17, 2022

Stability with mixed H /passivity performance analysis of fractional-order neutral delayed Markovian jumping neural networks

  • Narasimman Padmaja and Pagavathi Gounder Balasubramaniam EMAIL logo

Abstract

A detailed survey of existing works on fractional-order nonlinear systems reveals the fact that practically no results exist on stability or any performance analysis of Markovian jumping fractional-order systems (FOSs) in general. The main reason is the theory of infinitesimal generator used to estimate the derivative of Lyapunov–Krasovskii Functional (LKF) is not well-developed in the fractional domain. This shortage, in theory, is focussed in this manuscript. In this work, we provide a lemma that aids in analyzing the stability of fractional-order delayed systems via integer-order derivative of LKF. Using this lemma, by constructing a new suitable LKF and employing known integral inequalities, linear matrix inequality (LMI)-based sufficient conditions that ensure stability along with H /passive performance of the proposed fractional-order neural networks (FONNs) with Markovian jumping parameters are derived for the first time. Unlike the existing works, the results derived in the present study depend on the fractional order (FO) of the NNs. The importance of such order-dependent criteria is highlighted in numerical examples. Finally, the simulation results are given to show the reliability of the derived conditions.

MSC Code: 26A33; 34D23; 34K20; 93D05; 93C10

Corresponding author: Pagavathi Gounder Balasubramaniam, Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram 624 302, Tamil Nadu, India, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Received: 2021-12-02
Revised: 2022-08-14
Accepted: 2022-09-23
Published Online: 2022-10-17

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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