物理学报2011，Vol.60Issue(11)：1-6,6.
基于Cholesky分解的增量式RELM及其在时间序列预测中的应用
Incremental regularized extreme learning machine based on Cholesky factorization and its application to time series prediction
张弦 1王宏力1
作者信息
- 1. 第二炮兵工程学院自动控制工程系,西安710025
- 折叠
摘要
Abstract
In order to solve the hidden-layer neuron determination problem of regularized extreme learning machine （RELM） applied to chaotic time series prediction, a new algorithm based on Cholesky factorization is proposed. First, an RELM-based prediction model with one hidden-layer neuron is constructed and then a new hidden-layer neuron is added to the prediction model in each training step until the generalization performance of the prediction model reaches its peak value. Thus, the optimal network structure of the prediction model is determined. In the training procedure, Cholesky factorization is used to calculate the output weights of RELM. Experiments on chaotic time series prediction indicate that the algorithm can be effectively used to determine the optimal network strueture of RELM, and the prediction model trained by the algorithm has excellent performance in prediction accuracy and computational cost.关键词
神经网络/极端学习机/混沌时间序列/时间序列预测Key words
neural networks/extreme learning machine/chaotic time series/time series prediction分类
数理科学引用本文复制引用
张弦,王宏力..基于Cholesky分解的增量式RELM及其在时间序列预测中的应用[J].物理学报,2011,60(11):1-6,6.
