scholarly journals Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering

2015 ◽  
Vol 29 (11) ◽  
pp. 1411-1426 ◽  
Author(s):  
M. V. Kulikova ◽  
J. V. Tsyganova
Keyword(s):  
Kalman Filter ◽  
Adaptive Filtering ◽  
Gradient Based

Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

Navigation ◽  
2016 ◽  
Vol 63 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Negin Sokhandan ◽  
Ali Broumandan ◽  
James T. Curran ◽  
Gérard Lachapelle
Keyword(s):  
Adaptive Filtering ◽  
Stochastic Gradient ◽  
Range Estimation ◽  
Gradient Based

scholarly journals Noise control with Kalman filter for active headrest

2021 ◽  
Vol 39 (5) ◽  
pp. 937-944
Author(s):  
Lei WANG ◽  
Kean CHEN ◽  
Jian XU ◽  
Wang QI
Keyword(s):  
Kalman Filter ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Signal ◽  
State Equation ◽  
The State ◽  
Convergence Speed ◽  
State Variables ◽  
Gradient Based ◽  
Convergence Performance

A control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is established on the basis of virtual error sensing, and only the weight coefficients of the control filter are considered in the state variables. In order to ensure the convergence performance of the algorithm, an online updating strategy of KF parameters is proposed. The fast-array method is also introduced into the algorithm to reduce the computation. The simulation results show that the present strategy can improve the convergence speed and effectively reduce the noise signal at the virtual error point.

Extended Kalman Filter–Based Pruning Method for Recurrent Neural Networks

1998 ◽  
Vol 10 (6) ◽  
pp. 1481-1505 ◽  
Author(s):  
John Sum ◽  
Lai-wan Chan ◽  
Chi-sing Leung ◽  
Gilbert H. Young
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Recurrent Neural Networks ◽  
Posterior Probability ◽  
Linear Time ◽  
Learning Methods ◽  
Pruning Method ◽  
Gradient Based

Pruning is one of the effective techniques for improving the generalization error of neural networks. Existing pruning techniques are derived mainly from the viewpoint of energy minimization, which is commonly used in gradient-based learning methods. In recurrent networks, extended Kalman filter (EKF)–based training has been shown to be superior to gradient-based learning methods in terms of speed. This article explains a pruning procedure for recurrent neural networks using EKF training. The sensitivity of a posterior probability is used as a measure of the importance of a weight instead of error sensitivity since posterior probability density is readily obtained from this training method. The pruning procedure is tested using three problems: (1) the prediction of a simple linear time series, (2) the identification of a nonlinear system, and (3) the prediction of an exchange-rate time series. Simulation results demonstrate that the proposed pruning method is able to reduce the number of parameters and improve the generalization ability of a recurrent network.

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