论文信息 - Application of the Least Squares Support Vector Machine Based on Quantum Particle Swarm Optimization for Data Fitting of Small Samples

Application of the Least Squares Support Vector Machine Based on Quantum Particle Swarm Optimization for Data Fitting of Small Samples

In order to better observe the trend of small sample data, this paper based on that the least squares support vector machine (LS-SVM) algorithm has an outstanding performance in the data processing of small sample, presents a data fitting method for small sample. The quantum particle swarm optimization (QPSO) that has better global search ability is used to optimize the parameters of the least squares support vector machine, and establish the curve fitting model. According to error analysis, show that the method presented in this paper has a good application value.

Li Qing Fang | Ji Sheng Ma | Hai Ping Liu | Hong Kai Wang | Yan Feng Yang

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

[2] Shi Bian-xia. QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method , 2010 .

[3] Shao Hui-he. Application of support vector machines and least squares support vector machines to heart disease diagnoses , 2003 .

[4] Weiwu Yan,et al. Parallel decision models based on support vector machines and their application to distributed fault diagnosis , 2003, Proceedings of the 2003 American Control Conference, 2003..