Passivity Analysis for Switched Generalized Delayed Neural Networks

Sang, Hong; Nie, Hong; Fu, Jun

doi:10.1109/tcsii.2022.3223938

Cited by 3 publications

(1 citation statement)

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“…A switched system contains a certain number of subsystems (modes), and a switching signal is used to manage the switching behavior between subsystems. This has drawn great interest due to its wide application in the fields of motor systems, circuits, mechanical systems, power electronics, air traffic control [43–63], and so forth. Switched NNs are NNs that have a switching signal controlling their parameters.…”

Section: Introductionmentioning

confidence: 99%

Robust passivity analysis for uncertain stochastic switched inertial neural networks with time‐varying delay under a new state‐dependent switching law

Nasira Banu,

Banupriya,

Dhanya

2024

Math Methods in App Sciences

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This paper addresses the problems of passivity and robust passivity for a class of stochastic switched inertial neural networks (SSINNs) with time‐varying parametric uncertainties. First, the original inertial neural networks can be converted into first‐order differential systems using a suitable variable transformation approach. Moreover, by using a proper Lyapunov–Krasovskii functional (LKF) theory, Jensen's inequality, and the state‐dependent switching (SDS) law approach, several adequate criteria are derived in terms of linear matrix inequalities (LMIs) to ensure passivity and robust passivity analysis of SSINNs with parametric uncertainties and time‐varying delays. It is demonstrated that the developed SDS law may guarantee the passivity requirements of the above‐considered system made up of all unstable subnetworks. Furthermore, the gains are obtained by solving a set of LMIs that can be easily verified by some standard numerical packages. Ultimately, two examples with numerical simulations are provided to demonstrate the efficacy and feasibility of the suggested method.

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Section: Introductionmentioning

confidence: 99%