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Self-Adaptive Evolutionary Extreme Learning Machine.
- Source :
- Neural Processing Letters; Dec2012, Vol. 36 Issue 3, p285-305, 21p
- Publication Year :
- 2012
-
Abstract
- In this paper, we propose an improved learning algorithm named self-adaptive evolutionary extreme learning machine (SaE-ELM) for single hidden layer feedforward networks (SLFNs). In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore-Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine (E-ELM) and the different evolutionary Levenberg-Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13704621
- Volume :
- 36
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Neural Processing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 83307308
- Full Text :
- https://doi.org/10.1007/s11063-012-9236-y