Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation

This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizi...

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Veröffentlicht in:Neural networks Jg. 105; S. 154 - 165
Hauptverfasser: Selvaraj, P., Sakthivel, R., Kwon, O.M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.09.2018
Schlagworte:
Coupled stochastic neural networks
Finite-time synchronization
Markovian jumping parameters
Saturation effect
Stochastic coupling strength
Markovian jumping parameters
Saturation effect
Coupled stochastic neural networks
Finite-time synchronization
Stochastic coupling strength
ISSN:0893-6080, 1879-2782, 1879-2782
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Zusammenfassung:This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov–Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2018.05.004