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http://dx.doi.org/10.5391/JKIIS.2007.17.6.807

Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm  

Cho, Jae-Hoon (충북대학교 전기전자컴퓨터공학부)
Lee, Dae-Jong (충북대학교 BK21 충북정보기술사업단)
Chun, Myung-Geun (충북대학교 전기전자컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.6, 2007 , pp. 807-812 More about this Journal
Abstract
Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.
Keywords
Single-hidden-layer feedforward neural networks; Exterme Learning Machine; Moore Penrose generalized inverse; Bacterial foraging algorithm;
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