Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34916
Title: An independent component analysis based weight initialization method for multilayer perceptrons
Authors: Yam, YF
Leung, CT
Tam, PK
Siu, WC 
Keywords: Weight initialization
Multilayer perceptrons
Independent component analysis
Issue Date: 2002
Publisher: Elsevier
Source: Neurocomputing, 2002, v. 48, no. 1-4, p. 807-818 How to cite?
Journal: Neurocomputing
Abstract: A novel algorithm for determining the optimal initial weights of multilayer perceptrons based on an independent component analysis is developed. The algorithm is able to initialize the hidden layer weights that extract the salient feature components from the input data. The initial output layer weights are evaluated in such a way that the output neurons are kept inside the active region. Real-world benchmark problems were used for validating the proposed algorithm. The simulation results indicate that the proposed algorithm gets a substantial reduction in the required training time as compared with the other well-known weight initialization schemes. The proposed weight initialization method is capable of speeding up the learning process of multilayer perceptrons effectively.
URI: http://hdl.handle.net/10397/34916
ISSN: 0925-2312
DOI: 10.1016/S0925-2312(01)00674-9
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