Nonlinear time-series analysis with non-singleton fuzzy logic systems
Nonlinear time-series analysis with non-singleton fuzzy logic systems
复制标题
非单一模糊逻辑系统的非线性时间序列分析
DOI：
10.1109/cifer.1995.495252
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
J. Mendel
中科院分区：
文献类型：
--
作者：
G. C. Mouzouris;J. Mendel
我们开始研究非单一模糊逻辑系统(NSFLSS)在金融市场预测中的应用。利用NSFLS对任意函数的逼近能力，以及对噪声和不确定性的有效处理能力，分析了几个时间序列。首先，我们展示了如何构造NSFLS，并使用递归最小二乘或反向传播来训练它们。然后利用它们对加性噪声污染的离散混沌时间序列和连续混沌时间序列建立预测模型。最后，我们给出了一个例子，说明如何使用NSFLS来产生对商品未来价值的预测估计，并用线性回归来确定我们的结果的基线。我们的NSFLS的性能优于线性回归结果。
We initiate an investigation of the use of nonsingleton fuzzy logic systems (NSFLSs) in forecasting of financial markets. The abilities of NSFLSs to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze several time series. First we show how to construct NSFLSs and train them using recursive least squares, or backpropagation. Then we use them to build predictive models of discrete and continuous chaotic time series corrupted by additive noise. Finally, we present an example of how NSFLSs can be used to produce predicted estimates of future values of commodities, and baseline our results with linear regression. Our NSFLS outperforms the linear regression results.