Recently, CGNNs with Markovian jumping parameters have been extensively studied due to the fact that systems with Markovian jumping parameters are useful in modeling abrupt phenomena such as random failures, operating in different points of a nonlinear plant, and changing in the interconnections of subsystems [13 18].
Abrupt phenomena in modelling real-world systems indicate the importance of investigating systems with steep gradients.
As an important class of hybrid systems, hybrid stochastic differential equations (SDEs) (also known as SDEs with Markovian switching) have been widely employed to model many practical systems that have variable structures subject to random abrupt changes, which may result from abrupt phenomena such as random failures and repairs of components, sudden environment changes, etc.
Unfortunately, the stochastic disturbances in real world do not obey Gaussian noise, but they are variable structures subject to stochastic abrupt changes, which may come from abrupt phenomena.
A lot of difference systems have variable structures subject to stochastic abrupt changes, which may result from abrupt phenomena such as stochastic failures and repairs of the components, changes in the interconnections of subsystems, sudden environment changes, etc.
The systems have variable structures subject to stochastic abrupt changes, which may result from abrupt phenomena such as stochastic failures and repairs of the components, changes in the interconnections of subsystems, sudden environment changes, etc.
A lot of dynamical systems have variable structures subject to stochastic abrupt changes, which may result from abrupt phenomena such as stochastic failures and repairs of the components, changes in the interconnections of subsystems or sudden environment switching.
For networks with communication constraints, such random occurring coupling may be subject to random abrupt changes, which may have resulted from abrupt phenomena such as random failures, changes in the interconnections of subsystems and sudden environment changes, and so forth.
For this case, it is recognized that stochastic differential equations with Lévy noise are quite suitable to describe such stochastic abrupt phenomena.
There is also evidence that the nonseizure (inter-ictal) to seizure (ictal) transition is not an abrupt phenomenon [29].
