Run-by-Run Process Control of Metal Sputter Deposition: Combining Time Series and Extended Kalman Filter

2007 ◽  
Vol 20 (3) ◽  
pp. 278-285 ◽  
Author(s):  
Juhn-Horng Chen ◽  
Tzu-Wei Kuo ◽  
An-Chen Lee
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Process Control ◽  
Extended Kalman Filter ◽  
Sputter Deposition

scholarly journals Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Ghassane Benrhmach ◽  
Khalil Namir ◽  
Abdelwahed Namir ◽  
Jamal Bouyaghroumni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Financial Time Series ◽  
Broad Class ◽  
Financial Time ◽  
The Neural Network ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.

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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