Nonlinear Time Series Forecasting with Dynamic RBF Neural Networks

被引：1

作者：

Zhang, Dongqing ^{[1
]}

Ning, Xuanxi ^{[1
]}

Liu, Xueni ^{[1
]}

Han, Yubing ^{[2
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 210016, Jiangsu Prov, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Sch Elect Engn & Optoelect Tech, Nanjing 210016, Jiangsu Prov, Peoples R China

来源：

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

关键词：

Prediction; Radial Basis Function Neural Networks; Sequential Monte Carlo Methods;

D O I：

10.1109/WCICA.2008.4593999

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to cope with nonlinear time series, a variable structure radial basis function (RBF) networks model, in which the numbers of basis functions and input order vary over time, is proposed in this paper. Then sequential Monte Carlo (SMC) method is used for time series on-line prediction and corresponding algorithm is developed. At last, the data of weekly price of the shipbuilding steel product are analyzed, and experimental results indicate that the variable structure RBF networks model proposed is effective.

引用

页码：6988 / +

页数：3

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