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Erschienen in:
2007 | OriginalPaper | Buchkapitel
An Improved On-Line Sequential Learning Algorithm for Extreme Learning Machine
verfasst von : Bin Li, Jingming Wang, Yibin Li, Yong Song
Erschienen in: Advances in Neural Networks – ISNN 2007
Verlag: Springer Berlin Heidelberg
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This paper presents an efficient online sequential learning algorithm for extreme learning machine, which can learn data one by one. In this algorithm, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. In the online sequence, the algorithm updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least square algorithm. Simulations on benchmark problems demonstrate that the algorithm produces much better generalization performance than another online sequential extreme learning machine algorithm, or sometimes it has good performance than primitive extreme learning machine algorithm.