摘要
针对线性组合预测方法的局限性，提出了一种基于T－S模糊模型的非线性组合预测新方法，并给出了相应的反向传播学习算法．理论分析和应用实例表明：该方法具有很强的学习与泛化能力，在处理诸如非线性系统中时间序列的组合建模与预测方面有很好的应用价值．
In this paper, a new nonlinear combination forecasting method based on fuzzy Takagi-Sugeno model is presented to overcome the limitation in linear combination forecasting. Furthermore, the corresponding back propagation learning algorithm is put forward to identify the structure 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 methed are feasible and effective.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2000年第5期109-114,共6页
Systems Engineering-Theory & Practice
基金
国家自然科学基金!79770105
重庆市科委资助!5569
关键词
模糊模型
非线性组合预测
模糊系统
combination forecasting
fuzzy model
back propagation learning algorithm
