| A nonlinear combination forecasting method based on the fuzzy inference system |
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| 作者姓名： | 董景荣 YANG Jun |
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| 作者单位： | [1]DepartmentofMathematicsandComputerScience,ChongqingNormalUniversity,Chongqing,400047,P.R.China [2]CollgeofBusinessAdministration,ChongqingUniversity,Chongqing400044,P.R.China |
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| 基金项目： | Funded by the Excellent Young Teachers of MOE (350) and Chongqing Education Committee Foundation |
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| 摘 要： | It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones,However,the literature on combining forecasts has almost exclusively focused on linear combining forecasts.In this paper,a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series.Furthermore,the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system.Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.
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| 关 键 词： | 非线性联合预测方法 模糊推理系统 层次结构 自动控制 模糊控制 学习算法 |
A nonlinear combination forecasting method based on the fuzzy inference system |
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| DONG Jingrong,YANG Jun,YANG Xiutai.A nonlinear combination forecasting method based on the fuzzy inference system[J].Journal of Chongqing University,2002,1(2):78-82. |
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| Authors: | DONG Jingrong YANG Jun YANG Xiutai |
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| Institution: | DONG Jingrong1,YANG Jun2,YANG Xiutai2 1Department of Mathematics and Computer Science,Chongqing Normal University,Chongqing,400047,P.R. China 2College of Business Administration,Chongqing University,Chongqing,400044,P.R. China |
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| Abstract: | It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts. |
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| Keywords: | nonlinear combination forecasting fuzzy inference system hierarchical structure learning automata |
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