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Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction

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  • Li-Xin Wang

Abstract

A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multi-layer 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 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 WM Method. After the first-layer fuzzy systems are designed, we pass the data through the first layer to form a new data set and design the second-layer fuzzy systems based on this new data set 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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  • Li-Xin Wang, 2018. "Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction," Papers 1812.11226, arXiv.org, revised Aug 2019.
    • Handle: RePEc:arx:papers:1812.11226
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    Cited by:

    1. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
    2. Marjan Golob, 2023. "NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes," Mathematics, MDPI, vol. 11(2), pages 1-22, January.

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