Mathematics and Computer Science
Volume 1, Issue 1, May 2016, Pages: 1-4
Received: Mar. 7, 2016;
Accepted: Mar. 16, 2016;
Published: Apr. 10, 2016
Views 4509 Downloads 259
Weichao Jiao, College of Mathematic and Information, China West Normal University, Nanchong Sichuan, China
Junfei Dong, College of Mathematic and Information, China West Normal University, Nanchong Sichuan, China
Support vector machine is a machine learning algorithm with good performance, its parameters have an important influence on accuracy of classification, and parameters selection is becoming one of the main research areas of machine learning. This paper adopt support vector machine to recognize the characters of license plate. But in order to get good parameters of support vector machine, this paper has proposed a modified particles warm optimization algorithm to obtain the parameters of support vector machine. Experiments show that the proposed algorithm has higher recognition accuracy than others, the character recognition accuracy of training set is 99.95%, and character recognition accuracy of test set reaches 98.87%.
An Improved PSO-SVM Algorithm for License Plate Recognition, Mathematics and Computer Science.
Vol. 1, No. 1,
2016, pp. 1-4.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Wu Wei, Xinhan Huang, Qi-sen Zhang, etal. License plate character recognition based on template matching and neural network. Pattern Recognition and Aitificial Intelligence, 2001, 13(01): 123-127.
Min Wang, Xinhan Huang, Wu Wei, etal. License plate character recognition method for template matching and neural network. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2001, 29(03): 48-50.
Rongyan Guo, Xuehui Hu. Application of BP neural network in vehicle license plate character recognition. Computer simulation, 2010, 27(09): 299-301+350.
Xiaoxia Zheng, Feng Qian. Gaussian kernel SVM and model parameters Selection. Computer Engineering and Applications, 2006, 42(1): 77-79.
Xiaoyun Zhang, Yuncai Liu. Performance analysis of Gaussian kernel support vector machine. Computer Engineering, 2003, 29(8): 22-25.
Chao Guo, Weihua Song, Wei Wei. Stope roof stabilit y prediction based on both SVM and grid-search method. China Safety Science Journal, 2014, 24(8): 31-36.
Qingyi Li, Hao Zhou, Aping Lin, etal. Prediction of ash fusion temperature based on grid search and support vector machine. Journal of Zhejiang University (Engineering Science), 2011, 45(12): 2181-2187.
Wu CH, Tzeng GH, Goo YJ, etal. Areal-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications, 2007, 32(2): 397-408.
Min SH, Lee J, Han I. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 2006, 31(3); 652-660.
Yuanliang Pan, Jun Du, Weitao Hu. Air to ground target optimal tracking method by ant colony optimization-based SVM. Journal of Computational Information Systems, 2014, 10(5): 1805-1810.
Al Dulaimi H B A, Ku Mahamud. Solving SVM model selection problem using ACOR and IACOR. WSEAS Transactions on Computers, 2013, 12(9): 1109-2750.
Huang LC, Dun FJ. A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Applied Soft Computing, 2008, 8(4): 1381-1391.
Weisheng Fei, Junming Wang, Binyu Miao, etal. Particle swarm optimization based support vector machine for forecasting dissolved gases content in power transformer oil. Energy Conversion and Management, 2009, 50(6): 1604-1609.
Tao Wu. Kernels’ properties, tricks and its applications on obstacle detection. Changsha: College of Mechatronic Engineering and Automation, National University of Defense Technology, 2003: 18-23.