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....
| Hlavní autor: | |
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| Médium: | Diplomová práce |
| Jazyk: | English |
| Vydáno: |
2005
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| Témata: | |
| On-line přístup: | http://psasir.upm.edu.my/id/eprint/5851/1/FSKTM_2005_5%20IR.pdf |
| _version_ | 1825943940549836800 |
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| author | Tsan, Ken Yim |
| author_facet | Tsan, Ken Yim |
| author_sort | Tsan, Ken Yim |
| collection | UPM |
| 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 |
| first_indexed | 2024-03-06T07:08:07Z |
| format | Thesis |
| id | upm.eprints-5851 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:08:07Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | 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) |
| spellingShingle | Systems Analysis/ Operations Research Neural networks (Computer science) Tsan, Ken Yim An Improvement on Extended Kalman Filter for Neural Network Training |
| title | 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_short | An Improvement on Extended Kalman Filter for Neural Network Training |
| title_sort | improvement on extended kalman filter for neural network training |
| topic | Systems Analysis/ Operations Research Neural networks (Computer science) |
| url | http://psasir.upm.edu.my/id/eprint/5851/1/FSKTM_2005_5%20IR.pdf |
| work_keys_str_mv | AT tsankenyim animprovementonextendedkalmanfilterforneuralnetworktraining AT tsankenyim improvementonextendedkalmanfilterforneuralnetworktraining |