基于忆阻器的固定时间混沌通信方法

混沌信号具有隐蔽性、不可预测性和高度复杂等特点，特别适用于保密通信和扩频通信等领域。为有效解决有限时间同步控制过度依赖初始条件的问题，基于固定时间稳定性理论，通过设计状态反馈控制器给出了忆阻神经网络实现固定时间同步的充分条件，并对停息时间进行了精确估计。在此基础上，以驱动混沌忆阻神经网络为发射器，以具有不同初值的响应系统为接收器，采用混沌遮掩的方式实施信号加载，实现在固定时间内恢复加密信号，确保保密通信的安全性和时效性。

Fixed-Time Chaotic Communications Method Based on Memristors

Characterized with invisibility, unpredictability, and high complexity, chaotic signals are particularly suited to areas such as secure communications and spread spectrum communications. In order to effectively solve the problem that the finite-time synchronization control excessively depends on the initial conditions, based on the fixed-time stability theory, sufficient conditions are given for the memristive neural networks to achieve fixed-time synchronization by designing the state feedback controller and the settling time is accurately estimated. On this basis, with the driven chaotic memristive neural networks as transmitter and response systems with different initial values as receiver, signal loading is implemented using chaotic masking to recover encrypted signals within a fixed time, ensuring the security and timeliness of confidential communications.