Journal
NEURAL COMPUTING & APPLICATIONS
Volume 24, Issue 7-8, Pages 1487-1495Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-013-1385-z
Keywords
Extreme learning machine (ELM); Affinity propagation (AP); Radial basis function neural network (RBFNN)
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Funding
- National Basic Research Program of China [2013CB329502]
- National Natural Science Foundation of China [41074003]
- Opening Foundation of the Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences [IIP2010-1]
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In this paper, a novel self-adaptive extreme learning machine (ELM) based on affinity propagation (AP) is proposed to optimize the radial basis function neural network (RBFNN). As is well known, the parameters of original ELM which developed by G.-B. Huang are randomly determined. However, that cannot objectively obtain a set of optimal parameters of RBFNN trained by ELM algorithm for different realistic datasets. The AP algorithm can automatically produce a set of clustering centers for the different datasets. According to the results of AP, we can, respectively, get the cluster number and the radius value of each cluster. In that case, the above cluster number and radius value can be used to initialize the number and widths of hidden layer neurons in RBFNN and that is also the parameters of coefficient matrix H of ELM. This may successfully avoid the subjectivity prior knowledge and randomness of training RBFNN. Experimental results show that the method proposed in this thesis has a more powerful generalization capability than conventional ELM for an RBFNN.
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