Zheng T Y, Yang W H, Sun J, Xiong X Y, Li Z T, Zou X D
The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100000, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100000, China.
Sci Rep. 2021 Jan 13;11(1):997. doi: 10.1038/s41598-020-80339-5.
Reservoir computing (RC) is a recently introduced bio-inspired computational framework capable of excellent performances in the temporal data processing, owing to its derivation from the recurrent neural network (RNN). It is well-known for the fast and effective training scheme, as well as the ease of the hardware implementation, but also the problematic sensitivity of its performance to the optimizable architecture parameters. In this article, a particular time-delayed RC with a single clamped-clamped silicon beam resonator that exhibits a classical Duffing nonlinearity is presented and its optimization problem is studied. Specifically, we numerically analyze the nonlinear response of the resonator and find a quasi-linear bifurcation point shift of the driving voltage with the driving frequency sweeping, which is called Bifurcation Point Frequency Modulation (BPFM). Furthermore, we first proposed that this method can be used to find the optimal driving frequency of RC with a Duffing mechanical resonator for a given task, and then put forward a comprehensive optimization process. The high performance of RC presented on four typical tasks proves the feasibility of this optimization method. Finally, we envision the potential application of the method based on the BPFM in our future work to implement the RC with other mechanical oscillators.
储层计算（RC）是一种最近引入的受生物启发的计算框架，由于它源自递归神经网络（RNN），因此能够在时间数据处理方面表现出色。它以快速有效的训练方案以及易于硬件实现而闻名，但它的性能对可优化的架构参数也存在问题敏感性。在本文中，提出了一种具有单个两端固定的硅梁谐振器的特定延时RC，该谐振器表现出经典的杜芬非线性，并研究了其优化问题。具体而言，我们对谐振器的非线性响应进行了数值分析，并发现随着驱动频率扫描，驱动电压存在准线性分岔点偏移，这被称为分岔点频率调制（BPFM）。此外，我们首次提出该方法可用于为给定任务找到具有杜芬机械谐振器的RC的最佳驱动频率，然后提出了一个全面的优化过程。在四个典型任务上呈现的RC的高性能证明了这种优化方法的可行性。最后，我们设想基于BPFM的方法在我们未来的工作中用于与其他机械振荡器实现RC的潜在应用。
