A Hopf physical reservoir computer

Shougat, Md Raf E Ul; Li, Xiaofu; Mollik, Tushar; Perkins, Edmon

doi:10.1038/s41598-021-98982-x

Cited by 19 publications

(13 citation statements)

References 51 publications

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“…However, oscillators are capable of other types of computation as well, even without adaptive states. For instance, the classical, non-adaptive Hopf oscillator can be realized as a powerful, reconfigurable reservoir computer (Shougat et al 2021b(Shougat et al , 2022. In this reservoir computing architecture, the physics of the oscillator are utilized as a computational resource through machine learning.…”

Section: Introductionmentioning

confidence: 99%

Chaos in a Pendulum Adaptive Frequency Oscillator Circuit Experiment

Lİ

Beal

Dean

et al. 2023

Chaos Theory and Applications

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Adaptive oscillators can learn and encode information in dynamic, plastic states. The pendulum has recently been proposed as the base oscillator of an adaptive system. In a mechanical setup, the horizontally forced pendulum adaptive frequency oscillator seeks a resonance condition by modifying the length of the pendulum's rod. This system stores the external forcing frequency when the external amplitude is small, while it can store the resonance frequency, which is affected by the nonlinearity of the pendulum, when the external amplitude is large. Furthermore, for some frequency ranges, the pendulum adaptive frequency oscillator can exhibit chaotic motion when the amplitudes are large. This adaptive oscillator could be used as a smart vibratory energy harvester device, but this chaotic region could degrade its performance by using supplementary energy to modify the rod length. The pendulum adaptive frequency oscillator’s equations of motions are discussed, and a field-programmable analog array is used as an experimental realization of this system as an electronic circuit. Bifurcation diagrams are shown for both the numerical simulations and experiments, while period-3 motion is shown for the numerical simulations. As little work has been done on the stability of adaptive oscillators, the authors believe that this work is the first demonstration of chaos in an adaptive oscillator.

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Section: Introductionmentioning

confidence: 99%

Chaos in a Pendulum Adaptive Frequency Oscillator Circuit Experiment

Lİ

Beal

Dean

et al. 2023

Chaos Theory and Applications

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“…[29] More importantly, as the reservoir does not change during training, one can use a physical body as the reservoir and harvest its nonlinear and high-dimensional dynamic responses as the computational resource [30] (essentially, the physical body itself becomes the neural network). For example, PRC has been achieved in many biological and advanced material systems, such as photonic reservoirs, [31,32] memresistive reservoirs, [33,34] nonlinear oscillators, [35,36] and even living plants. [37] More recently, there is an emerging interest in reservoir computing with mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

Wang

2023

Advanced Intelligent Systems

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Herein, the cognitive capability of a simple, paper‐based Miura‐ori—using the physical reservoir computing framework—is experimentally examined to achieve different information perception tasks. The body dynamics of Miura‐ori (aka its vertices displacements), which is excited by a simple harmonic base excitation, can be exploited as the reservoir computing resource. By recording these dynamics with a high‐resolution camera and image processing program and then using linear regression for training, it is shown that the origami reservoir has sufficient computing capacity to estimate the weight and position of a payload. It can also recognize the input frequency and magnitude patterns. Furthermore, multitasking is achievable by simultaneously applying two targeted functions to the same reservoir state matrix. Therefore, it is demonstrated that Miura‐ori can assess the dynamic interactions between its body and ambient environment to extract meaningful information—an intelligent behavior in the mechanical domain. Given that Miura‐ori has been widely used to construct deployable structures, lightweight materials, and compliant robots, enabling such information perception tasks can add a new dimension to the functionality of such a versatile structure.

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“…Particularly, micromechanical resonator-based RC combines the advantages of the micro-electro-mechanical system (MEMS) devices [26,27] and the physical RC [28,29], such as small size, low consumption, compatibility with CMOS technology or MEMS sensors (MEMS accelerometers, MEMS pressure sensors, and so on), rendering it convenient to process sensing signals in the analog domain directly (especially signal identification and classification), and greatly reduces the amount of redundant terminal data and improves the security of information. Very recently, a single silicon beam resonator device was first proposed to achieve RC using a classical Duffing nonlinearity as the source of nonlinearity and was found to have huge potential applications in combining the functions of sensing and computing [23].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Recognition Task Performance of MEMS Resonator-Based Reservoir Computing System via Nonlinearity Tuning

et al. 2022

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Reservoir computing (RC) is a potential neuromorphic paradigm for physically realizing artificial intelligence systems in the Internet of Things society, owing to its well-known low training cost and compatibility with nonlinear devices. Micro-electro-mechanical system (MEMS) resonators exhibiting rich nonlinear dynamics and fading behaviors are promising candidates for high-performance hardware RC. Previously, we presented a non-delay-based RC using one single micromechanical resonator with hybrid nonlinear dynamics. Here, we innovatively introduce a nonlinear tuning strategy to analyze the computing properties (the processing speed and recognition accuracy) of the presented RC. Meanwhile, we numerically and experimentally analyze the influence of the hybrid nonlinear dynamics using the image classification task. Specifically, we study the transient nonlinear saturation phenomenon by fitting quality factors under different vacuums, as well as searching the optimal operating point (the edge of chaos) by the static bifurcation analysis and dynamic vibration numerical models of the Duffing nonlinearity. Our results in the optimal operation conditions experimentally achieved a high classification accuracy of (93 ± 1)% and several times faster than previous work on the handwritten digits recognition benchmark, profit from the perfect high signal-to-noise ratios (quality factor) and the nonlinearity of the dynamical variables.

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A Hopf physical reservoir computer

Cited by 19 publications

References 51 publications

Chaos in a Pendulum Adaptive Frequency Oscillator Circuit Experiment

Chaos in a Pendulum Adaptive Frequency Oscillator Circuit Experiment

Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

Enhancing the Recognition Task Performance of MEMS Resonator-Based Reservoir Computing System via Nonlinearity Tuning

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