摘要
基于数据挖掘思想,使用兴趣度度量和改进的梯度下降法,提出一种新的、具有自学习能力的模糊方法来建模和预测混沌时间序列.所提方法不仅能同时辨识模糊模型、调整其参数及确定输出空间的最优模糊子集,而且解决了梯度下降法中存在的收敛速度和振荡之间的冲突问题.仿真结果表明新方法是有效的、准确的,它能很好地辨识系统的特征,并且提供了一种混沌时间序列预测的新方法.*
On the basis of data mining, a new self-learning fuzzy method is developed to model and predict chaotic time series, by means of interest measure and improved gradient descent method. The proposed method can not only identify the fuzzy model, update its parameters and determine the optimal output fuzzy sets simultaneously, but also resolve the conflicts between convergence speed and oscillation existing in gradient descent method. Simulation results show the effeetiveness and accuracy of the proposed method. It can identify the system characteristics quite well and provide a new way to predict the chaotic time series.
出处
《信息与控制》
CSCD
北大核心
2005年第6期660-664,共5页
Information and Control
基金
国家科技攻关计划资助项目(2001BA204B01)
教育部骨干教师计划资助项目(69825106)
关键词
混沌
数据挖掘
预测
模糊模型
chaos
data mining
prediction
fuzzy model
