Passivity of Switched Recurrent Neural Networks With Time-Varying Delays

This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hystere...

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Published inIEEE transaction on neural networks and learning systems Vol. 26; no. 2; pp. 357 - 366
Main Authors Lian, Jie, Wang, Jun
Format Journal Article
LanguageEnglish
Published United States IEEE 01.02.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial Intelligence
Average dwell time
Biological neural networks
Computer Simulation
Delay
Delays
Hysteresis
hysteresis switching law
Law
Lyapunov functions
Lyapunov methods
Mathematical Computing
Neural networks
Neural Networks (Computer)
Passivity
Recurrent neural networks
Stochasticity
switched neural networks
Switches
Switching
Symmetric matrices
Time Factors
Vibration
hysteresis switching law
passivity
switched neural networks
Average dwell time
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Abstract This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.
AbstractList This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.
This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.
Author Jie Lian
Jun Wang
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passivity
switched neural networks
Average dwell time
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Snippet This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the...
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Publisher
StartPage 357
SubjectTerms Algorithms
Artificial Intelligence
Average dwell time
Biological neural networks
Computer Simulation
Delay
Delays
Hysteresis
hysteresis switching law
Law
Lyapunov functions
Lyapunov methods
Mathematical Computing
Neural networks
Neural Networks (Computer)
Passivity
Recurrent neural networks
Stochasticity
switched neural networks
Switches
Switching
Symmetric matrices
Time Factors
Vibration
Title Passivity of Switched Recurrent Neural Networks With Time-Varying Delays
URI https://ieeexplore.ieee.org/document/7001700
https://www.ncbi.nlm.nih.gov/pubmed/25576577
https://www.proquest.com/docview/1647300170
https://www.proquest.com/docview/1652395779
https://www.proquest.com/docview/1669891614
https://www.proquest.com/docview/1709183824
Volume 26
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