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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Vydané v:IEEE transactions on fuzzy systems Ročník 28; číslo 7; s. 1301 - 1314
Hlavný autor: Wang, Li-Xin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
ISSN:1063-6706, 1941-0034
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2930488