Performance Enhancement in Requirement Prioritization by Using Least-Squares-Based Random Genetic Algorithm

Author(s):  
Heena Ahuja ◽  
Sujata ◽  
Usha Batra
ROBOT ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 72 ◽  
Author(s):  
Yuhu DU ◽  
Jiancheng FANG ◽  
Wei SHENG ◽  
Xusheng LEI

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.


2015 ◽  
Vol 35 (8) ◽  
pp. 0830001
Author(s):  
董永芳 Dong Yongfang ◽  
孟耀勇 Meng Yaoyong ◽  
张平丽 Zhang Pingli ◽  
文玮 Wen Wei ◽  
李娜 Li Na

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