NDLI: Predict time series using extended, unscented, and cubature Kalman filters based on feed-forward neural network algorithm

Content Provider	IEEE Xplore Digital Library
Author	Safarinejadian, B. Tajeddini, M.A. Ramezani, A.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Shiraz Univ. of Technol., Shiraz, Iran (Safarinejadian, B.; Ramezani, A.) \|\| Dept. of Electr. & Comput. Eng., Tehran Univ., Tehran, Iran (Tajeddini, M.A.)
Abstract	Successful application of artificial neural networks (ANNs) in prediction of nonlinear systems with a high degree has made extensive studies in this field. Time-varying, dynamic properties, as well as internal noise, are the problems that occur in prediction of nonlinear systems. The advantages of nonlinear filtering algorithms are controlling the addictive noise and high accurate estimation during the implementation process. This paper explores the use of time-series forecasting algorithms by combining nonlinear filters with feedforward neural networks. In this paper, space state equations and measurement of non-linear filters are written based on the weights and output of the ANNs. In other word, the extended, unscented, and cubature Kalman filters is used for training the feed-forward neural network (FNN). To evaluate the proposed method, these techniques have been used to forecast Mackey-Glass time series. The overall accuracy of cubature Kalman filter is better than the two others. The results are also confirmed by computer simulations.
Starting Page	159
Ending Page	164
File Size	291319
Page Count	6
File Format	PDF
e-ISBN	9781479931170
DOI	10.1109/ICCIAutom.2013.6912827
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-28
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Neural networks Noise Feed-forward neural networks Time series forecasting Prediction algorithms Mathematical model Kalman filters Equations Non-linear Kalman filters
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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