An accurate and robust online sequential learning algorithm for feedforward networks

被引：0

作者：

Lu, Cheng-Bo ^{[1
]}

Mei, Ying ^{[1
]}

机构：

[1] Faculty of Engineering and Design, Lishui University, Lishui,Zhejiang,323000, China

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2015年 / 49卷 / 08期

关键词：

Learning systems - Network layers - E-learning - Knowledge acquisition - Learning algorithms;

D O I：

10.16183/j.cnki.jsjtu.2015.08.010

中图分类号：

学科分类号：

摘要：

In this paper, a kind of accurate and robust online sequential learning algorithm was proposed for single hidden layer feedforward networks. The algorithm is referred to as online sequential discrete Fourier transform-extreme learning machine (OS-DFT-ELM). This approach is able to learn data one-by-one or chunk-by-chunk. During the growth of the data, input weights and output weights are adjusted incrementally. The proposed algorithm has a higher degree of accuracy and robustness compared to the approach referred to as online sequential-extreme learning machine (OS-ELM). Two simulation examples were presented to show the excellent performance of the proposed approach. ©, 2015, Shanghai Jiao Tong University. All right reserved.

引用

页码：1137 / 1143

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