Extended dissipative analysis for memristive neural networks with two-delay components via a generalized delay-product-type Lyapunov-Krasovskii functional

Zirui Zhao; Wenjuan Lin; Zirui Zhao; Wenjuan Lin

doi:10.3934/math.20231573

AIMS Mathematics

2023, Volume 8, Issue 12: 30777-30789. doi: 10.3934/math.20231573

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Research article

Extended dissipative analysis for memristive neural networks with two-delay components via a generalized delay-product-type Lyapunov-Krasovskii functional

Zirui Zhao ^1,2,
Wenjuan Lin ^{1,2
,
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1.
School of Automation, Qingdao University, Qingdao 266071, China
2.
Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China

Received: 24 August 2023 Revised: 08 November 2023 Accepted: 09 November 2023 Published: 15 November 2023
MSC : 93C10, 93D05, 93D30

In this study, we deal with the problem of extended dissipativity analysis for memristive neural networks (MNNs) with two-delay components. The goal is to get less conservative extended dissipativity criteria for delayed MNNs. An improved Lyapunov-Krasovskii functional (LKF) with some generalized delay-product-type terms is constructed based on the dynamic delay interval (DDI) method. Moreover, the derivative of the created LKF is estimated using the integral inequality technique, which includes the information of higher-order time-varying delay. Then, sufficient conditions are attained in terms of linear matrix inequalities (LMIs) to pledge the extended dissipative of MNNs via the new negative definite conditions of matrix-valued cubic polynomials. Finally, a numerical example is shown to prove the value and advantage of the presented approach.
- memristive neural networks,
- two-delay components,
- extended dissipativity,
- dynamic delay interval method
Citation: Zirui Zhao, Wenjuan Lin. Extended dissipative analysis for memristive neural networks with two-delay components via a generalized delay-product-type Lyapunov-Krasovskii functional[J]. AIMS Mathematics, 2023, 8(12): 30777-30789. doi: 10.3934/math.20231573

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Abstract

In this study, we deal with the problem of extended dissipativity analysis for memristive neural networks (MNNs) with two-delay components. The goal is to get less conservative extended dissipativity criteria for delayed MNNs. An improved Lyapunov-Krasovskii functional (LKF) with some generalized delay-product-type terms is constructed based on the dynamic delay interval (DDI) method. Moreover, the derivative of the created LKF is estimated using the integral inequality technique, which includes the information of higher-order time-varying delay. Then, sufficient conditions are attained in terms of linear matrix inequalities (LMIs) to pledge the extended dissipative of MNNs via the new negative definite conditions of matrix-valued cubic polynomials. Finally, a numerical example is shown to prove the value and advantage of the presented approach.

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