Publisher : Korean Institute of Intelligent Systems
DOI : 10.5391/JKIIS.2016.26.1.087
Title & Authors
Optimization of Fuzzy Learning Machine by Using Particle Swarm Optimization Roh, Seok-Beom; Wang, Jihong; Kim, Yong-Soo; Ahn, Tae-Chon;
In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.
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