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This paper proposed a new model of coupled neural networks. In this model, each node system is a neural network with Markovian jumping parameters and interval time-varying delay. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. Moreover, the coupling strength is a positive random variable which is not required to be any special form.
Based on the properties of random variables, the difficult problem of dealing with the two random variables (random coupling fashion and random coupling strength) is well solved by using special techniques. By designing a novel Lyapunov functional, using some inequalities, and the properties of random variables, several delay-dependent synchronization criteria are derived for the coupled networks of continuous-time version. In our synchronization criteria, the derivative of the time-varying delay can be any given value.
In implementing continuous-time Markovian neural networks for simulation or
Based on the properties of random variables, the difficult problem of dealing with the two random variables (random coupling fashion and random coupling strength) is well solved by using special techniques. By designing a novel Lyapunov functional, using some inequalities, and the properties of random variables, several delay-dependent synchronization criteria are derived for the coupled networks of continuous-time version. In our synchronization criteria, the derivative of the time-varying delay can be any given value.
In implementing continuous-time Markovian neural networks for simulation or
