Expectation maximization estimation algorithm for Hammerstein models with non-Gaussian noise and random time delay from dual-rate sampled-data

2018 ◽  
Vol 73 ◽  
pp. 135-144 ◽  
Author(s):  
Junxia Ma ◽  
Jing Chen ◽  
Weili Xiong ◽  
Feng Ding
Keyword(s):  
Time Delay ◽  
Gaussian Noise ◽  
Expectation Maximization ◽  
Estimation Algorithm ◽  
Random Time ◽  
Sampled Data ◽  
Hammerstein Models ◽  
Non Gaussian

scholarly journals Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

2020 ◽  
Vol 37 (3) ◽  
pp. 449-465 ◽  
Author(s):  
Jeffrey J. Early ◽  
Adam M. Sykulski
Keyword(s):  
Gaussian Noise ◽  
A Priori ◽  
Smoothing Splines ◽  
Sampled Data ◽  
Trajectory Data ◽  
Tension Parameter ◽  
Comprehensive Method ◽  
Gps Trajectory Data ◽  
Non Gaussian ◽  
Irregularly Sampled Data

AbstractA comprehensive method is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers that are typical in global positioning system (GPS) signals. We demonstrate the effectiveness of our methods on GPS trajectory data obtained from oceanographic floating instruments known as drifters.

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].

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

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

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

Time delay and non-Gaussian noise-enhanced stability of foraging colony system

2020 ◽  
Vol 553 ◽  
pp. 124253 ◽  
Author(s):  
Jian-Li Wu ◽  
Wei-Long Duan ◽  
Yuhui Luo ◽  
Fengzao Yang
Keyword(s):  
Time Delay ◽  
Gaussian Noise ◽  
Non Gaussian ◽  
Enhanced Stability
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