Online Chaotic Time Series Prediction Based on Square Root Kalman Filter Extreme Learning Machine

被引：0

作者：

Feng, Shoubo ^{[1
]}

Xu, Meiling ^{[1
]}

Han, Min ^{[1
]}

机构：

[1] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV | 2017年 / 10637卷

基金：

中国国家自然科学基金;

关键词：

Time series prediction; Square root Kalman filter; Extreme learning machine; Convergence analysis;

D O I：

10.1007/978-3-319-70093-9_42

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we proposed a novel neural network prediction model based on extreme learning machine for online chaotic time series prediction problems. The model is characterized by robustness and generalization. The initial weights are initialized by orthogonal matrix to improve the generalization performance and the output weights are updated by square root Kalman filter. The convergence of the algorithm is proved by Lyapunov stability theorem. Simulations based on artificial and real-life data sets demonstrate the effectiveness of the proposed model.

引用

页码：402 / 409

页数：8

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