Prediction of turning points for chaotic time series using ensemble ANN model

被引：2

作者：

Li, Xiuquan ^{[1
]}

Deng, Zhidong ^{[1
]}

机构：

[1] Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Dept Comp Sci, Beijing 100084, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

Ensemble neural network; chaotic time series; turning points prediction; GA;

D O I：

10.1109/WCICA.2008.4593474

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A machine learning approach to predict turning points for chaotic time series was proposed through incorporating chaotic analysis into ensemble artificial neural network (ANN) modeling. The EM-like parameter learning algorithm for ensemble ANN model was presented. We then gave a new GA-based threshold optimization procedure using out-of-sample validation. The proposed approach was demonstrated on the benchmark chaotic time series like Mackey-Glass system. Our experimental results show significant improvement in performance over ANN model alone.

引用

页码：3459 / 3464

页数：6

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