Signal Denoising Based on Duffing Oscillators System

Luo, Wenmao; Cui, Yingliu

doi:10.1109/access.2020.2992503

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of the chaotic behavior of nonlinear dynamic systems such as oscillators has been a popular research interest (Liang et al, 2020;Wang et al, 2020;Corinto et al, 2021). Extensive investigation of some popular oscillators proposed by Chua (Petras, 2010), Duffing (Luo and Cui, 2020;Kabziński, 2018), Lorenz (Kanchana et al, 2020), Rayleigh (Pan and Das, 2018), Van der Pol (Giresse and Crépin, 2017), or Tamaševičius (Ueta and Tamura, 2012) help mimic dynamic characteristics embedded in physical systems. Van der Pol's and Duffing's oscillators are chaotic oscillators having two states.…”

Section: Context Of Researchmentioning

confidence: 99%

Forecasting models for chaotic fractional-order oscillators using neural networks

Bingi¹,

Prusty²

2021

View full text Add to dashboard Cite

This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing's, Van der Pol's, Tamaševičius's and Chua's, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model's out-of-sample forecasting plots show the best tracking ability to approximate future state values.

show abstract

Section: Context Of Researchmentioning

confidence: 99%

Forecasting models for chaotic fractional-order oscillators using neural networks

Bingi¹,

Prusty²

2021

View full text Add to dashboard Cite

show abstract

“…The Duffing oscillator has excellent performance in detecting sinusoidal signal frequency under strong interference conditions [18]- [21]. The core problem of using Duffing oscillator to detect sinusoidal signal is to identify the system state transition efficiently.…”

Section: Introductionmentioning

confidence: 99%

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

et al. 2022

View full text Add to dashboard Cite

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.INDEX TERMS Foreign objects debris (FOD), millimeter-wave (MMW) radar, false alarm elimination, Duffing oscillator, quantitative detection method.

show abstract

Fast-slow dynamics with non-zero sliding characteristics related to pitchfork bifurcation delay in the parametrically excited Duffing system with frequency switching

Jiang,

Han,

2024

Nonlinear Dyn

View full text Add to dashboard Cite

Signal Denoising Based on Duffing Oscillators System

Cited by 5 publications

References 32 publications

Forecasting models for chaotic fractional-order oscillators using neural networks

Forecasting models for chaotic fractional-order oscillators using neural networks

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

Fast-slow dynamics with non-zero sliding characteristics related to pitchfork bifurcation delay in the parametrically excited Duffing system with frequency switching

Contact Info

Product

Resources

About