Exponential passive filter design for switched neural networks with time-delay and reaction-diffusion terms

This paper investigates the problem of exponential passive filter design for switched neural networks with time-delay and reaction-diffusion terms. With the aid of a suitable Lyapunov–Krasovskii functional and some inequalities, a linear matrix inequality-based design method is developed that not only makes the filtering error system exponentially stable but also forces it to be passive from external interference to output error. Then, the filter design is extended to the complex-valued case via separating the system into real-valued and complex-valued parts. Finally, a numerical example is utilized to illustrate the effectiveness of the filter design methods for the real-valued and complex-valued cases, respectively.

Exponential Stability Analysis and Application of Parameters Switched Neural Networks Via Intermittent Observation and Feedback Control

This paper deals with a type of the exponential stability problem for the switched neural networks with timevarying delays driven by Brownian noise. As a prerequisite to main theorem, the existence and uniqueness of the solution to the main system are proved via contraction map theory. Based on intermittent observation control, the stability trajectory of the switched neural networks with time-varying delays is obtained. Employing stochastic analysis method, the exponential stability conditions are established via applying It^o formula and the matched pair technique. A numerical example for the main system with respect to intermittent observation control is provided to illustrate the effectiveness of results and potential of the proposed techniques. Meanwhile, the feasibility of stability control in multiagents system is verified by the method obtained.