Fast Training Algorithms for Deep Convolutional Fuzzy Systems With Application to Stock Index Prediction
A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multilayer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS b...
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| Veröffentlicht in: | IEEE transactions on fuzzy systems Jg. 28; H. 7; S. 1301 - 1314 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1063-6706, 1941-0034 |
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| Abstract | A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multilayer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS based on an input-output data pairs, we propose a bottom-up layer-by-layer scheme. Specifically, by viewing each of the first-layer fuzzy systems as a weak estimator of the output based only on a very small portion of the input variables, we design these fuzzy systems using the Wang-Mendel method. After the first-layer fuzzy systems are designed, we pass the data through the first layer to form a new dataset and design the second-layer fuzzy systems based on this new dataset in the same way as designing the first-layer fuzzy systems. Repeating this process layer-by-layer, we design the whole DCFS. We also propose a DCFS with parameter sharing to save memory and computation. We apply the DCFS models to predict a synthetic chaotic plus random time-series and the real Hang Seng Index of the Hong Kong stock market. |
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| AbstractList | A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multilayer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS based on an input–output data pairs, we propose a bottom–up layer-by-layer scheme. Specifically, by viewing each of the first-layer fuzzy systems as a weak estimator of the output based only on a very small portion of the input variables, we design these fuzzy systems using the Wang–Mendel method. After the first-layer fuzzy systems are designed, we pass the data through the first layer to form a new dataset and design the second-layer fuzzy systems based on this new dataset in the same way as designing the first-layer fuzzy systems. Repeating this process layer-by-layer, we design the whole DCFS. We also propose a DCFS with parameter sharing to save memory and computation. We apply the DCFS models to predict a synthetic chaotic plus random time-series and the real Hang Seng Index of the Hong Kong stock market. |
| Author | Wang, Li-Xin |
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| SubjectTerms | Algorithms Computational modeling Datasets Deep learning Fuzzy sets Fuzzy systems hierarchical fuzzy systems Input variables Multilayers Prediction algorithms stock index prediction Stock market indexes Training Wang–Mendel (WM) method Windows |
| Title | Fast Training Algorithms for Deep Convolutional Fuzzy Systems With Application to Stock Index Prediction |
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