Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

López, Cristian; Zhong, Wei; Lu, Siliang; Cong, Feiyun; Cortese, Ignacio

doi:10.1016/j.jsv.2017.08.043

Cited by 49 publications

(17 citation statements)

References 28 publications

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“…As we consider the system parameter tuning SR as a special nonlinear filter, it is a tenet that the more the tuning parameters (system complexity), the better output response. Similar results can also be found like multi-parameters tuning [49], improved potentials [30,50,51], coupled systems [31,32], etc. Hence, one of the contribution of this paper is to explain and lead the further explore of nonlinear filter.…”

Section: Discussionsupporting

confidence: 77%

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Dong

Shen

et al. 2020

Sensors

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Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter a o p t = 1 . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to H 1 (periodic signal with noise) and H 0 (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).

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Section: Discussionsupporting

confidence: 77%

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Dong

Shen

et al. 2020

Sensors

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“…As predicted by the SR scheme (see also [12]), it was shown that the oscillation of the FHN neuron becomes more closely correlated with the subthreshold periodic input current at some optimal level of noise intensity. The stochastic analysis of the FHN neuron and other neuron models has been extensively studied in recent years with various types of noises, multistability, and even how SR can be controlled; see, for example, [13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Coherent neural oscillations induced by weak synaptic noise

Yamakou

Jost

2018

Nonlinear Dyn

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We analyze the effect of synaptic noise on the dynamics of the FitzHugh-Nagumo (FHN) neuron model. In our deterministic parameter regime, a limit cycle solution cannot emerge through a singular Hopf bifurcation, but such a limit cycle can nevertheless arise as a stochastic effect, as a consequence of weak synaptic noise in a regime of strong timescale separation (ε → 0) between the slow and fast variables of the model. We investigate the mechanism behind this phenomenon, known as self-induced stochastic resonance (SISR) (Muratov et al. in Physica D 210:227-240, 2005), by using multiple-time perturbation techniques and by analyzing the escape mechanism of the random trajectories from the stable manifolds of the model equation. Even though SISR occurs only in the limit as the singular parameter ε → 0, decreasing ε does not increase the coherence of the oscillations in the FHN model, but rather increases the interval of the noise amplitude σ for which coherence occurs. This is in contrast to the dynamical system studied in Mura- 2005). Moreover, the phenomenon is robust under parameter tuning. Numerical simulations exhibit the results predicted by the theoretical analysis. In fact, our analysis together with that in Yamakou and Jost (Weak noise-induced transitions with inhibition and modulation of neural oscillations, 2017) reveals that the FHN model can support different stochastic resonance phenomena. While it had been known (Pikovsky and Kurths in Phys Rev Lett 78:775-778, 1997) that coherence resonance can occur when the slow variable is subjected to noise, we show that, when noise is added to the fast variable, two other types, inverse stochastic resonance and SISR, may emerge in the same weak noise limit and that the transition between these phenomena can be induced by varying a simple parameter.

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“…In terms of underdamped SR, Lu et al proposed a bistable underdamped SR method, which adjusted the system parameters by variable step size to achieve the maximum SNR output [38]. López proposed underdamped SR based on the FitzHugh-Nagumo potential and used an improved particle swarm optimization algorithm to achieve optimal parameter matching, the effectiveness of that method was verified by simulation and bearing experiments [39]. Lei et al used the synergistic effect of the vibration signal, potential structure, and damping factor and used the weighted SNR as the evaluation index of the output signal to achieve weak signal extraction [40].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2019

IEEE Access

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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 stable-state matching underdamped stochastic resonance (SMUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

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Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

Cited by 49 publications

References 28 publications

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Coherent neural oscillations induced by weak synaptic noise

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

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