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

摘要

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.