A novel diversiform stochastic resonance of a domain wall and its performance at different states

2016 ◽  
Vol 30 (09) ◽  
pp. 1650167
Author(s):  
Haibin Zhang ◽  
Kesai Ouyang ◽  
Qingbo He ◽  
Fanrang Kong
Keyword(s):  
Domain Wall ◽  
Stochastic Resonance ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Linear Response Theory ◽  
Theoretical Derivation ◽  
Additive Gaussian White Noise ◽  
Analytical Expressions ◽  
Stable Stage

The response of an underdamped stochastic resonance (SR) with a new pining potential model of domain wall (DW) in ferromagnetic strips driven by additive Gaussian white noise to an additive weak harmonic forcing is investigated. We address that the new nonlinear system can be converted between bi-stable and mono-stable freely by tuning the system parameters. Analytical expressions of signal-to-noise ratio (SNR) of the bi-stable stage is obtained based on the linear response theory. In addition, another type of SR, which occurs when the system is mono-stable, is also reported with the intrinsic frequency derived analytically. The SR in mono-stable stage confirms to the typical physical resonance better with frequency-selection characteristic. Numerical simulation of both stages is carried out with outputs conforming to the theoretical derivation. Owing to the diversity of potential model, the new system possesses considerable merits for engineering applications.

scholarly journals Stochastic Resonance in a Multistable System Driven by Gaussian Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Peiming Shi ◽  
Pei Li ◽  
Shujun An ◽  
Dongying Han
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
State Theory ◽  
Elimination Theory ◽  
Resonance System ◽  
System Parameters ◽  
Nonmonotonic Function

Stochastic resonance (SR) is investigated in a multistable system driven by Gaussian white noise. Using adiabatic elimination theory and three-state theory, the signal-to-noise ratio (SNR) is derived. We find the effects of the noise intensity and the resonance system parametersb,c, anddon the SNR; the results show that SNR is a nonmonotonic function of the noise intensity; therefore, a multistable SR is found in this system, and the value of the peak changes with changing the system parameters.

INVESTIGATION OF STOCHASTIC RESONANCE IN MONOSTABLE SYSTEMS

2008 ◽  
Vol 18 (09) ◽  
pp. 2833-2839 ◽  
Author(s):  
N. V. AGUDOV ◽  
A. V. KRICHIGIN
Keyword(s):  
Stochastic Resonance ◽  
Output Signal ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Periodic Signal ◽  
Signal Power ◽  
Signal To Noise ◽  
Input Noise ◽  
Power Amplification

The phenomena of stochastic resonance is studied in overdamped nonlinear monostable systems driven by a periodic signal and Gaussian white noise. It is shown that the signal power amplification as a function of input noise intensity can be different depending on nonlinearity: it can monotonically grow, decrease and it can reach a maximum at certain value of the noise intensity. Nevertheless, the output signal to noise ratio is shown to be always a decreasing function of input noise intensity.

scholarly journals A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

2019 ◽  
Author(s):  
Zhixing Li ◽  
Xiandong Liu ◽  
Tian He ◽  
Yingchun Shan
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Ant Colony Algorithm ◽  
Periodic Potential ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Noise Energy ◽  
Gear Fault ◽  
Potential Structure

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

scholarly journals HIGH SIGNAL-TO-NOISE RATIO GAIN BY STOCHASTIC RESONANCE IN A DOUBLE WELL

2001 ◽  
Vol 01 (03) ◽  
pp. L181-L188 ◽  
Author(s):  
ZOLTAN GINGL ◽  
PETER MAKRA ◽  
ROBERT VAJTAI
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Total Power ◽  
High Signal ◽  
Signal To Noise ◽  
Simulation Techniques ◽  
Response Range ◽  
Signal Simulation ◽  
Noise Ratio

We demonstrate that signal-to-noise ratio (SNR) can be significantly improved by stochastic resonance in a double well potential. The overdamped dynamical system was studied using mixed signal simulation techniques. The system was driven by wideband Gaussian white noise and a periodic pulse train with variable amplitude and duty cycle. Operating the system in the non-linear response range, we obtained SNR gains much greater than unity. In addition to the classical SNR definition, the ratio of the total power of the signal to the power of the noise part was also measured and it showed better signal improvement.

STOCHASTIC RESONANCE OF A LINEAR SYSTEM INDUCED BY DICHOTOMOUS NOISE

2008 ◽  
Vol 22 (06) ◽  
pp. 697-708
Author(s):  
YU-RONG ZHOU ◽  
FENG GUO ◽  
SHI-QI JIANG ◽  
XIAO-FENG PANG
Keyword(s):  
Linear System ◽  
External Field ◽  
Stochastic Resonance ◽  
Linear Response ◽  
Signal To Noise Ratio ◽  
Linear Response Theory ◽  
Resonance Phenomenon ◽  
Field Frequency ◽  
Dichotomous Noise ◽  
Stochastic Resonance Phenomenon

The stochastic resonance phenomenon in a linear system subject to multiplicative and additive dichotomous noise is investigated. By the use of the linear-response theory and the properties of the dichotomous noise, the exact expressions have been found for the first two moments and the signal-to-noise ratio (SNR). It is shown that the SNR is a non-monotonic function of the correlation time of the additive dichotomous noise, and it varies non-monotonically with the bias of the external field, with the intensity and asymmetry of the multiplicative dichotomous noise, as well as with the external field frequency. Moreover, the SNR depends on the intensity of the cross noise between the multiplicative and additive dichotomous noise, as well as on the strength and asymmetry of the additive dichotomous noise.

Stochastic resonance in a periodic potential system driven by cross-correlated noises and periodic signal

2019 ◽  
Vol 33 (28) ◽  
pp. 1950338
Author(s):  
Yongfeng Guo ◽  
Xiaojuan Lou ◽  
Qiang Dong ◽  
Linjie Wang
Keyword(s):  
Stochastic Resonance ◽  
Periodic Potential ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Signal Amplitude ◽  
Periodic Signal ◽  
Gaussian Colored Noise ◽  
Potential System ◽  
Noise Cross Correlation ◽  
Correlated Noises

In this paper, the stochastic resonance (SR) in a periodic potential system driven by cross-correlated noises and periodic signal is investigated. The signal-to-noise ratio (SNR) is used to characterize the SR. Using the algorithm of fourth-order Runge–Kutta, we obtain the curves of SNR for different parameters. The effects of some system parameters, additive Gaussian white noise and multiplicative Gaussian colored noise intensity on SR are characterized by analyzing SNR curves. When increasing system parameter and noise cross-correlation strength in SNR-D, the SR of the system can be enhanced. However, the SR will be weakened by increasing other parameters. Otherwise, the phenomena in SNR-Q are opposite to in SNR-D when increasing signal amplitude and correlation time.

Stochastic resonance in FitzHugh–Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise

2017 ◽  
Vol 31 (32) ◽  
pp. 1750264 ◽  
Author(s):  
Yong-Feng Guo ◽  
Bei Xi ◽  
Fang Wei ◽  
Jian-Guo Tan
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Neural System ◽  
Integral Approach ◽  
Stationary Probability Distribution ◽  
Non Gaussian ◽  
Two State Model

In this paper, the phenomenon of stochastic resonance in FitzHugh–Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

Research and application of tristable stochastic resonance based on harmonic and pinning potential model

2022 ◽  
pp. 107754632110586
Author(s):  
Lifang He ◽  
Yilin Liu ◽  
Gang Zhang
Keyword(s):  
Stochastic Resonance ◽  
Potential Function ◽  
First Passage Time ◽  
Gaussian White Noise ◽  
Reference Value ◽  
Linear Response Theory ◽  
Passage Time ◽  
Two Dimensional ◽  
Dimensional Model ◽  
One Dimensional

In view of the unique potential barrier and complex potential function of the pining model, as well as the lack of researches on two-dimensional stochastic resonance, two new potential tristable models are proposed: one-dimensional tristable model and two-dimensional tristable model. The stochastic resonance mechanism and application of two potential systems under Gaussian white noise and weak external driving force are discussed and the differences and advantages of the two systems are analyzed in detail for the first time. First, the potential function and mean first passage time are analyzed. Second, according to the linear response theory, the probability flow method is used to calculate the spectral amplification. The effects of system parameters on spectral amplification of the two models are studied, and the two models are compared. Finally, the two models are applied to the detection of actual bearing fault signals together with the classical tristable system and the performance is compared. Both algorithms can detect fault signals effectively, but the two-dimensional model has better amplitude and difference, and the one-dimensional model has less interference burrs. The theoretical basis and reference value of the system are provided for further application in practical engineering testing.

scholarly journals Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Haibin Zhang ◽  
Qingbo He ◽  
Siliang Lu ◽  
Fanrang Kong
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Weak Signal Detection ◽  
Gaussian Potential ◽  
System Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Proposed Model

This work aims for a new stochastic resonance (SR) model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP) and Gaussian potential (GP) instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR) system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR) can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

scholarly journals Parallel Array Bistable Stochastic Resonance System with Independent Input and Its Signal-to-Noise Ratio Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Li ◽  
Hanzhi Lu ◽  
Yanyan Zuo
Keyword(s):  
Stochastic Resonance ◽  
Performance Improvement ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Bistable System ◽  
Resonance System ◽  
Signal To Noise ◽  
Parallel Array ◽  
Independent Components ◽  
Noise Ratio

We study the design enhancement of the bistable stochastic resonance (SR) performance on sinusoidal signal and Gaussian white noise. The bistable system is known to show an SR property; however the performance improvement is limited. Our work presents two main contributions: first, we proposed a parallel array bistable system with independent components and averaged output; second, we give a deduction of the output signal-to-noise ratio (SNR) for this system to show the performance. Our examples show the enhancement of the system and how different parameters influence the performance of the proposed parallel array.

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