Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation

This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a nove...

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Published inIEEE transaction on neural networks and learning systems Vol. 34; no. 5; pp. 2682 - 2692
Main Authors Shen, Hao, Hu, Xiaohui, Wang, Jing, Cao, Jinde, Qian, Wenhua
Format Journal Article
LanguageEnglish
Published United States IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Abstract This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
AbstractList This work explores the [Formula Omitted] synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the [Formula Omitted] performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
This work explores the H synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H∞ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H∞ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
Author Cao, Jinde
Hu, Xiaohui
Shen, Hao
Qian, Wenhua
Wang, Jing
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  organization: School of Computer Science and Engineering, Yunnan University, Kunming, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/34487505$$D View this record in MEDLINE/PubMed
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PublicationTitle IEEE transaction on neural networks and learning systems
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Snippet This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed...
This work explores the H synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain...
This work explores the [Formula Omitted] synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints...
This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain...
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SubjectTerms Control systems
Control systems design
Controllers
Convexity
Double-layer switching regulation
Dwell time
Frequency modulation
H-infinity control
Markov analysis
Markov chains
Markov jump neural networks
Markov processes
Mathematical models
Neural networks
non-fragile synchronization
Optimization
Regulation
singularly perturbed coupled neural networks (SPCNNs)
Stochasticity
Switches
Switching
Synchronism
Synchronization
Transition probabilities
Title Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation
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https://www.ncbi.nlm.nih.gov/pubmed/34487505
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Volume 34
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