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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| Format: | Final Year Project |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/55039 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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