摘要
一、引言自从Ｊ．Ｍ．Ｂａｔｅｓ和Ｃ．Ｗ．Ｊ．Ｇｒａｎｇｅｒ首次提出组合预测方法［１］以来，组合预测的研究已经取得很大的进展，文献［２～６］对此有比较详细的综述和评价。根据集结或组合各单项预测模型的方式不同，组合预测一般可分为线性组合预测和非线性组合预...
Abstract In this paper,a new nonlinear combination forecasting method based on fuzzy Takagi Sugeno model system is presented to overcome the limitation in linear combination forecasting. Furthermore, the corresponding gradient descent learning algorithm is put forward to identify the parameter of the fuzzy model and partitions of fuzzy subsets. Theoretical analysis and forecasting examples all show that the new techniques has reinforcement learning properties and universalized Capabilities. With respect to combined modeling and forecasting of non stationary time Series in nonlinear systems, which have some uncertainties, the method are feasible and effective.
出处
《统计研究》
CSSCI
北大核心
1999年第8期55-58,共4页
Statistical Research
基金
国家自然科学基金
