Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 119-122.doi: 10.11896/j.issn.1002-137X.2017.6A.026

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Research on Tax Forecasting Model Based on PSO and Least Squares Support Vector Machine

ZHANG Shu-juan, DENG Xiu-qin and LIU Bo   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Aiming at the tax revenue forecast for the existence of nonlinearity,instability and economic factors that affect multiple complexities,this paper offered to use the method of least squares support vector regression machine to predict the tax revenue of Guangdong conghua,and established the mathematical model.As the model parameters anddirectly affect the quality of support vector machine,so the author ingeniously incorporated the idea of particle swarm optimization algorithm,and PSO for parameters optimization was used to ensure the accuracy and stability of the forecasting model.The simulation experimental results show that with respect to each reference model,using the PSO for parameters optimization of least squares support vector regression machine accuracy has improved significantly,illustrates the validity and practicability of the model.

Key words: Least squares support vector regression,Particle swarm optimization,Tax forecasting

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