英文标题：
《Fast Training Algorithms for Deep Convolutional Fuzzy Systems with
  Application to Stock Index Prediction》
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作者：
Li-Xin Wang
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最新提交年份：
2019
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英文摘要：
  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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中文摘要：
高维输入空间上的深卷积模糊系统（DCFS）是许多低维模糊系统的多层连接，其中低维模糊系统的输入变量是通过跨层输入空间的移动窗口选择的。为了设计基于输入输出数据对的DCF，我们提出了一种自下而上的逐层方案。具体而言，通过将每个第一层模糊系统视为仅基于很小部分输入变量的输出弱估计量，我们使用WM方法设计这些模糊系统。在设计了第一层模糊系统之后，我们将数据通过第一层来形成一个新的数据集，并以与设计第一层模糊系统相同的方式基于该新数据集来设计第二层模糊系统。我们逐层重复这个过程来设计整个DCF。我们还提出了一种具有参数共享的DCFS，以节省内存和计算量。我们应用DCFS模型预测了一个合成的混沌加随机时间序列和香港股市的实际恒生指数。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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