Statistical analysis of the time-delay digital tanlock loop in the presence of Gaussian noise

2002 ◽  
Author(s):  
Z.M. Hussain ◽  
B. Boashash
Keyword(s):  
Statistical Analysis ◽  
Time Delay ◽  
Gaussian Noise

scholarly journals On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 584
Author(s):  
Riccardo Rossi ◽  
Andrea Murari ◽  
Pasquale Gaudio
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Delay ◽  
Causal Relationship ◽  
Gaussian Noise ◽  
The Other ◽  
Causal Relationships ◽  
Indirect Coupling ◽  
Active Research

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series.

Statistical analysis of the electrical breakdown time delay distributions in krypton

2006 ◽  
Vol 13 (8) ◽  
pp. 083502 ◽  
Author(s):  
Čedomir A. Maluckov ◽  
Jugoslav P. Karamarković ◽  
Miodrag K. Radović ◽  
Momčilo M. Pejović
Keyword(s):  
Statistical Analysis ◽  
Time Delay ◽  
Electrical Breakdown ◽  
Breakdown Time

Stochastic Kinetics for a FitzHugh–Nagumo Neural System with Time Delay Driven by Non-Gaussian Noise and a Multiplicative Periodic Signal

2019 ◽  
Vol 18 (04) ◽  
pp. 1950027 ◽  
Author(s):  
Kang-Kang Wang ◽  
De-Cai Zong ◽  
Hui Ye ◽  
Ya-Jun Wang
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Neural System ◽  
Noise Correlation ◽  
Stable States ◽  
The Stability ◽  
Non Gaussian ◽  
System With Time Delay

In the present paper, the stability and the phenomena of stochastic resonance (SR) for a FitzHugh–Nagumo (FHN) system with time delay driven by a multiplicative non-Gaussian noise and an additive Gaussian white noise are investigated. By using the fast descent method, unified colored noise approximation and the two-state theory for the SR, the expressions for the stationary probability density function (SPDF) and the signal-to-noise ratio (SNR) are obtained. The research results show that the two noise intensities and time delay can always decrease the probability density at the two stable states and impair the stability of the neural system; while the noise correlation time [Formula: see text] can increase the probability density around both stable states and consolidate the stability of the neural system. Furthermore, the other noise correlation time [Formula: see text] can increase the probability at the resting state, but reduce that around the excited state. With respect to the SNR, it is discovered that the two noise strengths can both weaken the SR effect, while time delay [Formula: see text] and the departure parameter [Formula: see text] will always amplify the SR phenomenon. Moreover, the noise correlation time [Formula: see text] can motivate the SR effect, but not alter the peak value of the SNR. What’s most interesting is that the other noise correlation time [Formula: see text] can not only stimulate the SR phenomenon, but also results in the occurrence of two resonant peaks, whose heights are simultaneously improved because of the action of [Formula: see text].

Cooperative target localization using multiple UAVs with out-of-sequence measurements

2017 ◽  
Vol 89 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Yuliang Bai ◽  
Naigang Cui
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Gaussian Noise ◽  
Nonlinear Filtering ◽  
Planck Equation ◽  
Target Localization ◽  
Content Type ◽  
Fokker Planck Equation ◽  
Delay Measurement ◽  
Fokker Planck

Purpose The time delay would occurs when the measurements of multiple unmanned aerial vehicles (UAVs) are transmitted to the date processing center during cooperative target localization. This problem is often named as the out-of-sequence measurement (OOSM) problem. This paper aims to present a nonlinear filtering based on solving the Fokker–Planck equation to address the issue of OOSM. Design/methodology/approach According to the arrival time of measurement, the proposed nonlinear filtering can be divided into two parts. The non-delay measurement would be fused in the first part, in which the Fokker–Planck equation is utilized to propagate the conditional probability density function in the forward form. The time delay measurement is fused in the second part, in which the Fokker–Planck is used in the backward form approximately. The Bayes formula is applied in both parts during the measurement update. Findings Under the Bayesian filtering framework, this nonlinear filtering is not only suitable for the Gaussian noise assumption but also for the non-Gaussian noise assumption. The nonlinear filtering is applied to the cooperative target localization problem. Simulation results show that the proposed filtering algorithm is superior to the previous Y algorithm. Practical implications In this paper, the research shows that a better performance can be obtained by fusing multiple UAV measurements and treating time delay in measurement with the proposed algorithm. Originality/value In this paper, the OOSM problem is settled based on solving the Fokker–Planck equation. Generally, the Fokker–Planck equation can be used to predict the probability density forward in time. However, to associate the current state with the state related to OOSM, it would be used to propagate the probability density backward either.

Influence of non-Gaussian noise on the coherent feed-forward loop with time delay

2019 ◽  
Vol 129 ◽  
pp. 46-55
Author(s):  
Min Wang ◽  
Yuwen Fang ◽  
Yuhui Luo ◽  
Fengzao Yang ◽  
Chunhua Zeng ◽  
...  
Keyword(s):  
Time Delay ◽  
Gaussian Noise ◽  
Feed Forward ◽  
Feed Forward Loop ◽  
Non Gaussian

Non-Gaussian noise-weakened stability in a foraging colony system with time delay

2018 ◽  
Vol 492 ◽  
pp. 851-870 ◽  
Author(s):  
Xiaohui Dong ◽  
Chunhua Zeng ◽  
Fengzao Yang ◽  
Lin Guan ◽  
Qingshuang Xie ◽  
...  
Keyword(s):  
Time Delay ◽  
Gaussian Noise ◽  
Non Gaussian ◽  
System With Time Delay
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