This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

This paper focuses on the design of a fuzzy adaptive event-triggered sampled-data control (AETSDC) scheme for stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks (MNNs) with reaction-diffusion terms (RDTs). Different from the existing T-S fuzzy MNNs, the reaction and diffusion phenomena are considered, which make the presented model more applicable. A fuzzy AETSDC scheme is proposed for the first time, in which different AETSDC mechanisms will be applied for different fuzzy rules. For each fuzzy rule, the corresponding AETSDC mechanism can be promptly adaptively adjusted based on the current and last sampled signals. So the fuzzy AETSDC scheme can effectively save the limited communication resources for the considered system. By introducing a suitable Lyapunov-Krasovskii functional (LKF), new stability and stabilization criteria are established for T-S fuzzy MNNs with RDTs. Meanwhile, the desired fuzzy AETSDC gains are obtained. Finally, simulation results are given to verify the superiority of the fuzzy AETSDC scheme and the effectiveness of the theoretical results.

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.