经济时间序列的非线性组合建模与预测方法研究

Research on Nonlinear Combination Modeling and Forecasting Method of Economic Time Series

基于模糊系统在紧立集中能够任意逼近非线性连续函数的特性，本文提出了一种基于Ｔａｋａｇｉ—Ｓｕｇｅｎｏ模糊规则基的非线性组合预测新方法，以克服线性组合预测方法在解决非平稳时间序列组合建模问题所遇到的困难和存在的不足，并采用相应的遗传算法确定模糊系统的参数及模糊子集的划分。理论分析和大量的应用实例表明：该方法具有很强的学习与泛化能力，在处理诸如经济时间序列这种具有一定程度不确性的非线性系统的组合建模与预测方面有很好的应用价值．

Based on the property that the fuzzy system can approximate any nonlinear continuous function in the compact supporting set, a new nonlinear combination forecasting method based on Takagi-Sugeno fuzzy rule bases is present to overcome the difficulties and drawbacks in combined modeling non-stationary time series by using linear Combination forecasting. Furthermore, the corresponding genetic algorithm is used to identify the Parameter of the fuzzy system and partitions of fuzzy subsets. Theoretical analysis and forecasting examples all show that the new technique has reinforcement learning properties and universalized capabilities. With respect to combined modeling and forecasting of economic time series in nonlinear systems, which have some uncertainties, the method is available.