Quantized Sampled-Data Control for Exponential Stabilization of Delayed Complex-Valued Neural Networks

This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. Based on the discrete-time Lyapunov stability theory, reciprocally convex approach, a sector bound approach, and some estimation techniques, a reduced conservative stabilization criterion is obtained to guarantee the exponential stabilization of the considered CVNNs. The desired quantized sampled-data controller is designed via converting the complex-valued linear matrix inequality into real-valued ones. The effectiveness of the derived criteria are shown via an illustrative simulation example.

Automated summary

This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. By utilizing stability theory and estimation techniques, a conservative stability criterion is obtained and a corresponding controller is designed, with simulation results demonstrating the effectiveness of the criteria.