Stochastic resonance in two coupled fractional oscillators with potential and coupling parameters subjected to quadratic asymmetric dichotomous noise

Vishwamittar,; Batra, Priyanka; Chopra, Ribhu

doi:10.1016/j.physa.2020.125148

Cited by 15 publications

(9 citation statements)

References 46 publications

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“…With the in-depth study of SR theory by scholars, many new SR models have emerged. By changing the order of the SR system equation, two-dimensional Duffing oscillator SR [33] and fractional SR [34] appear. By changing the model of the potential function, there are multi-stable potential [22], piecewise unsaturated potential [11], asymmetric potential [24] and so on.…”

Section: Analysis Flow Of the Aplsr Methodsmentioning

confidence: 99%

Adaptive progressive learning stochastic resonance for weak signal detection

zong¹,

Men²,

An³

et al. 2023

Meas. Sci. Technol.

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Stochastic resonance (SR) can enhance signals by using noise. This has attracted more attention in the field of weak signal detection. In practical applications, owing to the non-adjustability of noisy signals, SR is required to adjust the system parameters adaptively to satisfy the conditions of the SR phenomenon. In this paper, an adaptive progressive learning stochastic resonance (APLSR) method is proposed to improve the detection ability of weak signal, and the SR phenomenon is quantitatively defined. A theoretical learning framework is established with an improved reinforcement learning model by mapping the nonlinear system parameter space to a progressive learning set. By selecting a proper learning layer within a determined constraint range, the matching system parameters can be quickly and accurately searched to generate a desired optimal output. Numerical simulation results show that the signal energy and the output-SNR can be enhanced significantly, which reflects an excellent weak signal detection performance especially for low signal-to-noise ratio (SNR) conditions. Finally, a diagnosis on the outer race fault signals of rolling bearing confirms that the proposed method can effectively detect fault characteristics.

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Section: Analysis Flow Of the Aplsr Methodsmentioning

confidence: 99%

Adaptive progressive learning stochastic resonance for weak signal detection

zong¹,

Men²,

An³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…For example, SR has been observed in fractional-order systems subject to random mass fluctuation and random frequency fluctuation [17][18][19][20][21]. Since many systems are coupled by particles while the above studies only consider a single particle, neglecting the interaction between particles, then the resonant behaviors of coupled fractional systems were studied [22][23][24][25][26][27][28]. Peng et al studied the resonant behaviors of two coupled harmonic oscillators with mass fluctuation modelled by trichotomous noise, and found that the output amplitude gain of the system is related to the coupling strength and the system reaches its optimum when it is coupled [22].…”

Section: Introductionmentioning

confidence: 99%

“…He and Tu et al discussed the resonant behavior of particles in a coupled fractional-order system with fluctuating damping, and the results indicate that the coupling strength has a significant effect on the output amplitude gain [26,27]. The resonance of particles in two coupled fractional oscillators with fluctuating frequency and fluctuating input signals affected by dichotomous noise was studied in [28].…”

Section: Introductionmentioning

confidence: 99%

A coupled fractional-order system with fluctuating frequency and its application in bearing fault diagnosis

He¹,

Liu²,

Jiang³

2023

Phys. Scr.

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In this paper, a coupled fractional-order system with fluctuating frequency driven by different periodic signals under various damping strength is investigated. Firstly, based on the Shapiro-Loginov formula and Laplace transform method, the expressions for the output amplitude gain (OAG) of the two subsystems are derived and the resonant behaviors of particles are analyzed. The OAG exhibits various resonance behaviors in response to variations in system parameters, input signals and dichotomous noise, including parameter-induced stochastic resonance, bona-fide resonance and stochastic resonance. Especially, the average behavior of the two output signals is synchronized when two subsystems’ input signals and damping strengths are equal, which is verified in the numerical simulation. Finally, the proposed system is applied to the bearing fault diagnosis to evaluate its engineering application value. The results prove that the system is effective in diagnosing fault signals and has excellent performance.

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“…Generally speaking, as a kind of system interference factor, noise breaks the original stability of the system, thus bringing adverse and harmful effects to the system. However, studies at home and abroad show that the introduction of noise in nonlinear systems improves the information processing ability of the nervous system and makes the system more stable [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Coherence Resonance Behavior of FitzHugh‐Nagumo Neurons Induced by Electromagnetic Field Driven by Phase Noise

Zheng

2022

Discrete Dynamics in Nature and Society

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Noise exists widely in the nervous system, and plays a crucial role in the nervous system information processing. Noise can not only enhance but also weaken the ability of the nervous system to process information. Neurons are in a complex and variable electromagnetic field. Electromagnetic induction plays an important role in regulating the changes of neuronal membrane potential. Therefore, this paper simulates the electromagnetic field environment of the nervous system with a memristor and analyses the rich coherence resonance behavior of FitzHugh-Nagumo (FHN) neuron system under the drive of phase noise. By taking the amplitude, period and noise intensity of phase noise as the main parameters and the parameters of memristor as auxiliary parameters, the two-parameter changes are made from the angle of the amplitude and period of phase noise, the amplitude and intensity of phase noise, and the noise intensity and period of phase noise, respectively. The dynamic behaviors of coherent resonance of FHN neuron system are analyzed from the amplitude and period, amplitude and intensity as well as intensity and period of phase noise, respectively. When the amplitude and period of the phase noise and the intensity and period of the phase noise are used as independent variables for the two-parameter analysis, the FHN neuron system shows rich dynamic behaviors such as coherence mono-resonance, coherence bi-resonance and coherence multi-resonance. Especially when the amplitude and period of phase noise change as two-parameter, the system presents a coherence resonance of discharge pattern with period-adding cluster discharge at the valley. When the amplitude and intensity of phase noise are taken as independent variables for two-parameter analysis, FHN neuronal system presents single or dual coherence resonance at any value of noise intensity with the change of phase noise amplitude. The simulated results show that the FHN neuron system demonstrates rich coherence resonance behaviors under the drive of phase noise when the effect of electromagnetic induction in the nervous system is simulated by memristor.

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Stochastic resonance in two coupled fractional oscillators with potential and coupling parameters subjected to quadratic asymmetric dichotomous noise

Cited by 15 publications

References 46 publications

Adaptive progressive learning stochastic resonance for weak signal detection

Adaptive progressive learning stochastic resonance for weak signal detection

A coupled fractional-order system with fluctuating frequency and its application in bearing fault diagnosis

Coherence Resonance Behavior of FitzHugh‐Nagumo Neurons Induced by Electromagnetic Field Driven by Phase Noise

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