An Improved LS-SVM Based on Quantum PSO Algorithm and Its Application

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.

[1]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  I. Chuang,et al.  Quantum Computation and Quantum Information: Bibliography , 2010 .

[5]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[7]  Dong Yao,et al.  An Approach to Attribute Reduction Based on Attribute Similarity , 2005 .

[8]  William Stafford Noble,et al.  Support vector machine , 2013 .

[9]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).