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The synchronization of reaction-diffusion neural networks with state and spatial couplings is investigated in this article, and the time-varying delay and stochastic disturbances are considered in the proposed systems. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Here, we suggest a spatial sampled-data controller design, where the sampled-data (in space) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in space are bounded. Based on the Lyapunov stability theory, Young's and Wirtinger's inequalities techniques, some sufficient conditions are presented to synchronize the hybrid coupling reaction-diffusion neural networks with stochastic disturbances. Finally, the efficiency of the derived criteria will be demonstrated by resorting to two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault detection (FD) problem for discrete-time stochastic systems with limited communication. A filter structure is proposed to construct the residual model for fault detection. For the limited network resources, a novel event-triggered strategy is employed to decrease the amount of data that is transmitted from the sensor to the filter. With the consideration of stochastic model and limited network resources, a novel event-based method is designed to ensure the residual system is stochastically stable and satisfies the desired fault sensitivity level and disturbance attenuation level. Compared with the traditional FD method, the proposed design strategy can not only achieve the desired fault detection performance, but also save the limited network resources. The effectiveness of design strategy is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered fault-detection problem is investigated for Itô-type stochastic systems. A filter structure is used to construct a residual model for fault detection. For limited network resources, a novel event-triggered strategy is proposed, while a trigger condition is utilized to determine and transmit the useful data through the network. Compared with the traditional periodic release strategy, the proposed design approach can significantly reduce the utilization of network bandwidth. A new condition is proposed to achieve the mean-square asymptotical stability of the residual model with the desired fault-detection objective. A novel algorithm is derived to obtain the parameters of the filter and the event detector. Two simulation cases are provided to illustrate the effectiveness of the design strategy. [ABSTRACT FROM AUTHOR]