Secure defense control for memristive recurrent neural networks under denial-of-service attacks with quantized sampled-data signals

This paper investigates the secure defense control problem for memristive recurrent neural networks (MRNNs) under denial-of-service (DoS) attacks. Based on quantizer and logical processor techniques, a new secure defense control strategy is designed to maintain the performance of MRNNs subject to DoS attacks. The secure defense control strategy can not only effectively capture information about the dwell time of each DoS attack, but also can effectively reduce the amount of data to be transmitted and save communication resources. By constructing a Lyapunov–Krasovskii functional that depends on sampled information at \(t_k\) $t_k$ and \(t_{k+1}\) $t_{k+1}$ , some new stability criteria are derived for MRNNs under DoS attacks. By solving a set of LMIs, the control gain of the quantized sampled-data controller can be obtained. Thus, the corresponding secure quantized sampled-data controller is designed for MRNNs under DoS attacks. When there is no quantizer, the secure defense control problem for MRNNs under DoS attacks is also studied. Two numerical examples are given to confirm the effectiveness of the main results.