Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach

2015 ◽  
Vol 70 ◽  
pp. 27-38 ◽  
Author(s):  
A. Chandrasekar ◽  
R. Rakkiyappan ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Transition Probabilities ◽  
Multiple Integral ◽  
Integral Approach ◽  
Markovian Jumping ◽  
Coupled Neural Networks ◽  
Impulsive Synchronization ◽  
Partly Unknown Transition Probabilities

scholarly journals Finite-Time Boundedness for a Class of Delayed Markovian Jumping Neural Networks with Partly Unknown Transition Probabilities

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Li Liang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Transition Probabilities ◽  
The State ◽  
Time Interval ◽  
Finite Time Interval ◽  
Markovian Jumping ◽  
Stochastic Boundedness ◽  
Partly Unknown Transition Probabilities ◽  
Finite Time Boundedness

This paper is concerned with the problem of finite-time boundedness for a class of delayed Markovian jumping neural networks with partly unknown transition probabilities. By introducing the appropriate stochastic Lyapunov-Krasovskii functional and the concept of stochastically finite-time stochastic boundedness for Markovian jumping neural networks, a new method is proposed to guarantee that the state trajectory remains in a bounded region of the state space over a prespecified finite-time interval. Finally, numerical examples are given to illustrate the effectiveness and reduced conservativeness of the proposed results.

Stochastic stability of fuzzy Markovian jump neural networks by multiple integral approach

2018 ◽  
Vol 11 (1) ◽  
pp. 81-105
Author(s):  
Cheng-De Zheng
Keyword(s):  
Neural Networks ◽  
Distributed Delay ◽  
Multiple Integral ◽  
Integral Approach ◽  
Time Varying ◽  
Markovian Jumping ◽  
Content Type ◽  
Continuously Distributed Delay ◽  
Time Varying Delay ◽  
Varying Delay

Purpose The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square. Design/methodology/approach The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions. Findings It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results. Originality/value The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

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