DocumentCode :
1589554
Title :
An Improved LS-SVM Based on Quantum PSO Algorithm and Its Application
Author :
Pan, Guofeng ; Xia, Kewen ; Dong, Yao ; Shi, Jin
Author_Institution :
Hebei Univ. of Technol., Tianjin
Volume :
2
fYear :
2007
Firstpage :
606
Lastpage :
610
Abstract :
In order to avoid the problem of inverse matrix calculation in LS-SVM algorithm, an improved LS-SVM based on quantum PSO algorithm is presented, the main process is to encode the particle swarm with quantum bit, then solve the linear equation set with the iterative quantum PSO algorithm. So the training velocity of LS- SVM algorithm is improved, the computer memory is saved, and the least square solution is always obtained. The actual application in Changqing oil-field indicates the application effect is better than that of classical SVM and LM neural network in oil layer recognition, the improved LS-SVM algorithm not only improves the accuracy of recognition, but also accelerates the velocity of convergence, and the result of oil layer recognition is fully accord with that of oil trial.
Keywords :
iterative methods; least squares approximations; matrix algebra; neural nets; particle swarm optimisation; petroleum industry; quantum computing; support vector machines; LM neural network; LS-SVM; computer memory; inverse matrix calculation; iterative quantum PSO algorithm; least square solution; linear equation set; oil layer recognition; particle swarm; quantum PSO algorithm; Acceleration; Application software; Equations; Iterative algorithms; Least squares methods; Particle swarm optimization; Petroleum; Quantum computing; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
Type :
conf
DOI :
10.1109/ICNC.2007.218
Filename :
4344422
Link To Document :
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