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Guo X, Yang W, Zheng T, Sun J, Xiong X, Wang Z, Zou X. Input-Output-Improved Reservoir Computing Based on Duffing Resonator Processing Dynamic Temperature Compensation for MEMS Resonant Accelerometer. MICROMACHINES 2023; 14:161. [PMID: 36677222 PMCID: PMC9864998 DOI: 10.3390/mi14010161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature changing due to the hysteresis of the temperature response of the accelerometer. To address this issue, we propose a novel reservoir computing (RC) structure based on a nonlinear silicon resonator, which is specifically improved for predicting dynamic information that is referred to as the input-output-improved reservoir computing (IOI-RC) algorithm. It combines the polynomial fitting with the RC on the input data mapping ensuring that the system always resides in the rich nonlinear state. Meanwhile, the output layer is also optimized by vector concatenation operation for higher memory capacity. Therefore, the new system has better performance in dynamic temperature compensation. In addition, the method is real-time, with easy hardware implementation that can be integrated with MEMS sensors. The experiment's result showed a 93% improvement in IOI-RC compared to raw data in a temperature range of -20-60 °C. The study confirmed the feasibility of RC in realizing dynamic temperature compensation precisely, which provides a potential real-time online temperature compensation method and a sensor system with edge computing.
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Affiliation(s)
- Xiaowei Guo
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wuhao Yang
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianyi Zheng
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Sun
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingyin Xiong
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
| | - Zheng Wang
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
| | - Xudong Zou
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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An Offset-Boostable Chaotic Oscillator with Broken Symmetry. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A new 3D offset-boostable symmetric system is proposed by an absolute value function introduced. The system seems to be more fragile and easier to the state of broken symmetry. Coexisting symmetric pairs of attractors get closer and closer, and finally get emerged together. Basins of attraction show how these coexisting attractors are arranged in phase space. All these coexisting attractors can be easily offset boosted in phase space by a single constant when the initial condition is revised accordingly. PSpice simulations prove all the phenomena.
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Jaurigue L, Robertson E, Wolters J, Lüdge K. Reservoir Computing with Delayed Input for Fast and Easy Optimisation. ENTROPY 2021; 23:e23121560. [PMID: 34945866 PMCID: PMC8700644 DOI: 10.3390/e23121560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 11/19/2021] [Accepted: 11/21/2021] [Indexed: 01/30/2023]
Abstract
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.
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Affiliation(s)
- Lina Jaurigue
- Institute of Theoretical Physics, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
- Correspondence:
| | - Elizabeth Robertson
- Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut fur Optische Sensorsysteme, Rutherfordstr. 2, 12489 Berlin, Germany; (E.R.); (J.W.)
- Institut für Optik und Atomare Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Janik Wolters
- Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut fur Optische Sensorsysteme, Rutherfordstr. 2, 12489 Berlin, Germany; (E.R.); (J.W.)
- Institut für Optik und Atomare Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Kathy Lüdge
- Institute of Physics, Technische Universität Ilmenau, Weimarer Str. 25, 98693 Ilmenau, Germany;
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Shougat MREU, Li X, Mollik T, Perkins E. A Hopf physical reservoir computer. Sci Rep 2021; 11:19465. [PMID: 34593935 PMCID: PMC8484469 DOI: 10.1038/s41598-021-98982-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/17/2021] [Indexed: 02/08/2023] Open
Abstract
Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler-Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.
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Affiliation(s)
- Md Raf E Ul Shougat
- LAB2701: Nonlinear Dynamics Laboratory, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, 27695, USA.
| | - XiaoFu Li
- LAB2701: Nonlinear Dynamics Laboratory, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, 27695, USA
| | - Tushar Mollik
- LAB2701: Nonlinear Dynamics Laboratory, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, 27695, USA
| | - Edmon Perkins
- LAB2701: Nonlinear Dynamics Laboratory, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, 27695, USA
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Zheng T, Yang W, Sun J, Xiong X, Wang Z, Li Z, Zou X. Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators. SENSORS (BASEL, SWITZERLAND) 2021; 21:2961. [PMID: 33922571 PMCID: PMC8122867 DOI: 10.3390/s21092961] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/15/2021] [Accepted: 04/20/2021] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Tianyi Zheng
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100010, China
| | - Wuhao Yang
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
| | - Jie Sun
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100010, China
| | - Xingyin Xiong
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
| | - Zheng Wang
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
| | - Zhitian Li
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
| | - Xudong Zou
- The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China; (T.Z.); (J.S.); (X.X.); (Z.W.); (Z.L.); (X.Z.)
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100010, China
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