Learning to forget in an online fuzzy neural network using dynamic forgetting window
This proposed architecture of using a Dynamic Window to compute the forgetting factor which would be able to provide thorough analysis of the self-reorganizing approach when applied to time-variant financial market such as S&P-500 index. When handling such large market, drifts and shifts in inev...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/55039 |
| _version_ | 1811697586823757824 |
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| author | Tan, Benjamin Kok Loong. |
| author2 | Quek Hiok Chai |
| author_facet | Quek Hiok Chai Tan, Benjamin Kok Loong. |
| author_sort | Tan, Benjamin Kok Loong. |
| collection | NTU |
| description | This proposed architecture of using a Dynamic Window to compute the forgetting factor which would be able to provide thorough analysis of the self-reorganizing approach when applied to time-variant financial market such as S&P-500 index. When handling such large market, drifts and shifts in inevitable and the system require the ability to have self-reorganizing abilities. To increase its accuracy, the proposed architecture uses the variable dynamic window to adjust the forgetting factor accordingly. |
| first_indexed | 2024-10-01T07:57:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/55039 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:57:37Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/550392023-03-03T20:37:15Z Learning to forget in an online fuzzy neural network using dynamic forgetting window Tan, Benjamin Kok Loong. Quek Hiok Chai School of Computer Engineering DRNTU::Engineering::Computer science and engineering This proposed architecture of using a Dynamic Window to compute the forgetting factor which would be able to provide thorough analysis of the self-reorganizing approach when applied to time-variant financial market such as S&P-500 index. When handling such large market, drifts and shifts in inevitable and the system require the ability to have self-reorganizing abilities. To increase its accuracy, the proposed architecture uses the variable dynamic window to adjust the forgetting factor accordingly. Bachelor of Engineering (Computer Engineering) 2013-12-04T08:40:53Z 2013-12-04T08:40:53Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/55039 en Nanyang Technological University 80 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Tan, Benjamin Kok Loong. Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title | Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title_full | Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title_fullStr | Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title_full_unstemmed | Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title_short | Learning to forget in an online fuzzy neural network using dynamic forgetting window |
| title_sort | learning to forget in an online fuzzy neural network using dynamic forgetting window |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/55039 |
| work_keys_str_mv | AT tanbenjaminkokloong learningtoforgetinanonlinefuzzyneuralnetworkusingdynamicforgettingwindow |