JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2011, Vol. 41 ›› Issue (6): 7-11.

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An improved extreme learning machine based on Akaike criterion

YIN Jian-chuan1,2, ZOU Zao-jian1,3, XU Feng1   

  1. 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
    2. Navigation College, Dalian Maritime University, Dalian 116026, China;
    3. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China
  • Received:2011-04-08 Online:2011-12-16 Published:2011-04-08

Abstract:

To reduce the dimension of a neural network and improve the generalization capability of the extreme learning machine (ELM) network, Akaike information criterion (AIC) was implemented to choose a suitable number of hidden units, and the modified Gram-Schmidt (MGS) method was also implemented to automatically adjust the network parameters. In comparison with the conventional ELM learning method on several commonly used regressor benchmark problems, the improved ELM algorithm could achieve  a  compact network with much faster training speed and satisfactory accuracy.

Key words: extreme learning machine, Akaike information criterion, modified Gram-Schmidt algorithm, feedforward neural network

CLC Number: 

  • TP183
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