| In recent decades,neural networks have attracted widespread attention due to their wide applications in signal processing,pattern recognition,etc.For the control community,the combination of neural network and sampling control provides a new method for solving control problems of complex nonlinear system.Some practical problems have been considered,such as the delay,failure,and packet loss,etc.The specific work is as follows:1)The synchronization problem of neural network based on packet loss and Markov jumping has been studied.Considering that the neural network system has packet loss in the real world,we establish a more complete neural network system model.Based on the Lyapunov stability theorem,the stability of systems under sampling control has been studied.Using wirtinger's inequality and free-matrix-based inequality,a novel LKF is designed to reduce the conservatism.The sampling controller is designed.Through sampling state feedback control,the master-slave synchronization and stability of the neural network system are ensured.The feasibility of the proposed method is verified by a case simulation.2)The master-slave synchronization problems of delayed neural networks with actuator failure based on stochastic sampled-data controller has been studied.In reality,the time delays inevitably occur in neural networks which will directly affect the stability of the system.In addition,the issue of actuator failure is also considered.Consider two different sampling periods whose occurrence probabilities follow the Bernoulli distribution,and it can be further extended to cases with multiple random sampling periods.By constructing a novel LKF,a sampling controller is designed.Using Jensen's inequality and Schur's theorem,the stability criterion with low conservative is obtained.Solving the LMIs,the controller gain matrix can be easily obtained.Finally,the simulation using the LMI toolbox in MATLAB verifies the effectiveness of the method in this article.Figure 17;Table 3;Reference 51... |
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