Decision Tree Prediction Algorithm Based on Variable Structure Neural Network Control

SHAN Dong-hong; LV Qiong-shuai; ZHAO Wei-ting

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Decision Tree Prediction Algorithm Based on Variable Structure Neural Network Control

更新时间：2026-09-04

- Decision Tree Prediction Algorithm Based on Variable Structure Neural Network Control
- Vol. 34, Issue 2, Pages: 92-96(2017)
- 作者机构：
  
  平顶山学院机构计算机学院(软件学院),河南,平顶山,467000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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单冬红, 吕琼帅, 赵伟艇. 基于变结构神经网络控制的决策树预测算法[J]. 微电子学与计算机, 2017, 34(2): 92-96.

SHAN Dong-hong, LV Qiong-shuai, ZHAO Wei-ting. Decision Tree Prediction Algorithm Based on Variable Structure Neural Network Control[J]. 2017, 34(2): 92-96.
单冬红, 吕琼帅, 赵伟艇. 基于变结构神经网络控制的决策树预测算法[J]. 微电子学与计算机, 2017, 34(2): 92-96. DOI：

SHAN Dong-hong, LV Qiong-shuai, ZHAO Wei-ting. Decision Tree Prediction Algorithm Based on Variable Structure Neural Network Control[J]. 2017, 34(2): 92-96. DOI：

摘要

为了提高统计数据时间序列准确预测的能力

针对当前的线性指数预测方法计算开销过大

且容易陷入局部最优的问题

提出一种基于变结构神经网络控制的决策树预测算法

对待处理的统计数据时间序列进行非线性特征相空间重构

在重构的高维特征相空间中进行决策树训练

提取统计数据信息流的主成分特征

对提取的特征参量采用变结构神经网络控制器进行伴随跟踪训练

进行数据聚类中心搜索和预测指向性误差的小扰动抑制

实现统计数据时间序列的准确预测.仿真结果表明

采用该算法进行统计数据预测的精度较高

收敛性较好

对数据信息流的走势拟合性能较好

性能优于传统方法.

Abstract

In order to improve the ability to predict the time sequence statistics

excessive computational overhead for the linear current index prediction method

and easy to fall into local optimum

this paper proposes a prediction algorithm of decision tree structure based on neural network control

treat the statistical data of time series nonlinear characteristics of phase space reconstruction

decision making tree training in high dimensional feature reconstruction of phase space

principal component feature extraction of statistical data on the flow of information

the extracted characteristic parameters of the variable structure neural network controller with tracking training

data clustering search and forecast the pointing error of small disturbance suppression

realize the accurate prediction of statistical data of time series. The simulation results show that the algorithm has higher precision and better convergence performance

and the performance of the data flow is better than that of the traditional method.

关键词

Keywords

references

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