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An Improvement on Extended Kalman Filter for Neural Network Training

Information overload has resulted in difficulties of managing and processing information. Reduction of data using well-defined techniques such as rough set may provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science....

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Bibliographic Details
Main Author: Tsan, Ken Yim
Format: Thesis
Language:English
Published: 2005
Subjects:
Systems Analysis/ Operations Research
Neural networks (Computer science)
Online Access:http://psasir.upm.edu.my/id/eprint/5851/1/FSKTM_2005_5%20IR.pdf
http://psasir.upm.edu.my/id/eprint/5851/
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spelling my.upm.eprints.58512022-01-06T03:03:12Z http://psasir.upm.edu.my/id/eprint/5851/ An Improvement on Extended Kalman Filter for Neural Network Training Tsan, Ken Yim Information overload has resulted in difficulties of managing and processing information. Reduction of data using well-defined techniques such as rough set may provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science. Artificial intelligence systems, such as neural network systems, are widely used to extract and infer knowledge from databases. This study explored the training of a neural network inference system using the extended Kalman filter (EKF) learning algorithm. The inference accuracy, inference duration and training performance of this extended Kalman filter neural network were compared with the standard back-propagation algorithm and an improved version of the back-propagation neural network learning algorithm. It was discovered that the extended Kalman filter trained neural network required less 2005-04 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/5851/1/FSKTM_2005_5%20IR.pdf Tsan, Ken Yim (2005) An Improvement on Extended Kalman Filter for Neural Network Training. Masters thesis, Universiti Putra Malaysia. Systems Analysis/ Operations Research Neural networks (Computer science)
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
topic Systems Analysis/ Operations Research
Neural networks (Computer science)
spellingShingle Systems Analysis/ Operations Research
Neural networks (Computer science)
Tsan, Ken Yim
An Improvement on Extended Kalman Filter for Neural Network Training
description Information overload has resulted in difficulties of managing and processing information. Reduction of data using well-defined techniques such as rough set may provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science. Artificial intelligence systems, such as neural network systems, are widely used to extract and infer knowledge from databases. This study explored the training of a neural network inference system using the extended Kalman filter (EKF) learning algorithm. The inference accuracy, inference duration and training performance of this extended Kalman filter neural network were compared with the standard back-propagation algorithm and an improved version of the back-propagation neural network learning algorithm. It was discovered that the extended Kalman filter trained neural network required less
format Thesis
author Tsan, Ken Yim
author_facet Tsan, Ken Yim
author_sort Tsan, Ken Yim
title An Improvement on Extended Kalman Filter for Neural Network Training
title_short An Improvement on Extended Kalman Filter for Neural Network Training
title_full An Improvement on Extended Kalman Filter for Neural Network Training
title_fullStr An Improvement on Extended Kalman Filter for Neural Network Training
title_full_unstemmed An Improvement on Extended Kalman Filter for Neural Network Training
title_sort improvement on extended kalman filter for neural network training
publishDate 2005
url http://psasir.upm.edu.my/id/eprint/5851/1/FSKTM_2005_5%20IR.pdf
http://psasir.upm.edu.my/id/eprint/5851/
_version_ 1724075260901326848
score 13.211869

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