DocumentCode :
3696210
Title :
Study on GA-based Training Algorithm for Extreme Learning Machine
Author :
Shaojian Song;Yao Wang;Xiaofeng Lin;Qingbao Huang
Author_Institution :
Sch. of Electr. Eng., Guangxi Univ., Nanning, China
Volume :
2
fYear :
2015
Firstpage :
132
Lastpage :
135
Abstract :
In view of the prediction accuracy of Extreme Learning Machine´s (ELM) is affected by its input weights and hidden layer neurons thresholds, an improved training method for ELM with Genetic Algorithms (GA-ELM) is proposed in this paper. In GA-ELM, after selection, crossover and mutation of Genetic Algorithm (GA), we will get the optimal weights and thresholds, in initial which are randomly obtained by ELM, then to enhance the generalization performance of ELM. The simulation results show that, compared with other algorithms, the GA-ELM has better prediction accuracy.
Keywords :
"Biological cells","Genetic algorithms","Training","Accuracy","Sociology","Statistics","Neurons"
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
Print_ISBN :
978-1-4799-8645-3
Type :
conf
DOI :
10.1109/IHMSC.2015.156
Filename :
7334934
Link To Document :
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