Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators

Sensors

Wuhao Yang¹  Zheng Wang²  Xingyin Xiong²  Jie Sun²  Zhitian Li²  Tianyi Zheng²  Xudong Zou² 

[1] School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100010, China;The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China;

关键词: reservoir computing;    coupled resonators;    MEMS;   

DOI  :  10.3390/s21092961

来源: DOAJ

【 摘 要 】

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.

【 授权许可】

Unknown