Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays

This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems ar...

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Main Authors: Han Pan, Wenbing Zhang, Luyang Yu
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/20/3870
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author Han Pan
Wenbing Zhang
Luyang Yu
author_facet Han Pan
Wenbing Zhang
Luyang Yu
author_sort Han Pan
collection DOAJ
description This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems are also taken into account and therefore, switched systems under consideration can take several switched systems as special cases. The comparison principle, the Halanay-like inequality, and the time-dependent switched Lyapunov function approach are used to obtain sufficient conditions to ensure that the considered SNNs with delays and partial state reset are exponentially stable. Numerical examples are provided to demonstrate the reliability of the developed results.
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spelling doaj.art-ad15c8abb7554033bd4e963cb4fca1122023-12-02T00:37:20ZengMDPI AGMathematics2227-73902022-10-011020387010.3390/math10203870Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying DelaysHan Pan0Wenbing Zhang1Luyang Yu2School of Mathematical Sciences, Yangzhou University, Yangzhou 225002, ChinaSchool of Mathematical Sciences, Yangzhou University, Yangzhou 225002, ChinaSchool of Mathematical Sciences, Yangzhou University, Yangzhou 225002, ChinaThis paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems are also taken into account and therefore, switched systems under consideration can take several switched systems as special cases. The comparison principle, the Halanay-like inequality, and the time-dependent switched Lyapunov function approach are used to obtain sufficient conditions to ensure that the considered SNNs with delays and partial state reset are exponentially stable. Numerical examples are provided to demonstrate the reliability of the developed results.https://www.mdpi.com/2227-7390/10/20/3870switched systemsLyapunov functionpartial state resetimpulsive systems
spellingShingle Han Pan
Wenbing Zhang
Luyang Yu
Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
Mathematics
switched systems
Lyapunov function
partial state reset
impulsive systems
title Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
title_full Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
title_fullStr Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
title_full_unstemmed Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
title_short Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays
title_sort exponential stability of switched neural networks with partial state reset and time varying delays
topic switched systems
Lyapunov function
partial state reset
impulsive systems
url https://www.mdpi.com/2227-7390/10/20/3870
work_keys_str_mv AT hanpan exponentialstabilityofswitchedneuralnetworkswithpartialstateresetandtimevaryingdelays
AT wenbingzhang exponentialstabilityofswitchedneuralnetworkswithpartialstateresetandtimevaryingdelays
AT luyangyu exponentialstabilityofswitchedneuralnetworkswithpartialstateresetandtimevaryingdelays