期刊
NEUROCOMPUTING
卷 69, 期 16-18, 页码 2369-2373出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2006.02.013
关键词
extreme learning machine; function approximation; the a priori information; generalization performance; convergence rate
In this letter, a class of improved extreme learning machines (ELM) encoding a priori information is proposed to obtain better generalization performance and much faster convergence rate for function approximation. According to Fourier series expansion theory, the hidden neurons activation functions in the improved ELM are sine and cosine functions. In addition, the improved ELM analytically determines the output weights of neural networks. Finally, experimental results are given to verify the efficiency and effectiveness of the improved ELM. (c) 2006 Elsevier B.V. All rights reserved.
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