In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications.

在这项工作中，提出了一种链式结构时延存储（CSTDR）计算，作为一种新型的基于机器学习的循环神经网络，用于同步混沌信号。与单个时延存储库相比，我们提出的 CSTDR 计算在同步混沌信号方面表现出出色的性能，实现了一个数量级的更高精度。讨论了模型的噪声考虑和最优参数设置。以CSTDR计算为核心，进一步设计了一种新颖的安全通信方案，其中“智能”接收器不同于传统的接收器，它可以自适应地同步到用于加密的混沌信号。该方案可以解决相同动力系统的设计约束和常规设置下发射机和接收机之间的耦合等问题。为进一步体现该方案的实际意义，采用现场可编程门阵列进行数字化实现，并通过包括图像和视频传输在内的真实示例进行了实验测试。这项工作揭示了开发基于机器学习的信号处理和通信应用程序。