摘要
在深入分析εp-邻近点能够避免伪邻近点产生的基础上,提出了区间邻近点的概念,它在有效防止伪邻近点产生的同时也建立起了一个邻近点列表,可以方便地从该列表中找出某一状态的邻近点集合,并给出了混沌时间序列的一种局域区间预测方法。该方法避免了经典局域预测法中每一步都要搜索历史数据寻找邻近点的过程,提高了局域预测的效率。
Based on ε^p-neighboring points, the interval neighboring point is defined for avoiding false neighboring points and building up a data list. According to the interval neighboring point, the local interval prediction method for chaotic time series is proposed. The list L for finding neighboring points is gradually established in the procedure of forecasting future values. It is convenient that all neighboring points are obtained from the list, instead of searching in history data. Also the method can continually update the list and supply new types of neighboring points for the list. And the effects of the length of history data and the number of small intervals on predictive performance are considered. In the end, our simulation shows the method is effective
出处
《陕西科技大学学报（自然科学版）》
2006年第1期53-57,共5页
Journal of Shaanxi University of Science & Technology
关键词
混沌时间序列
局域预测
历史数据长度
区间划分
chaotic time series, local prediction
length of history data
dividing interval
