基于粒子群优化的最小二乘支持向量机税收预测模型研究

针对税收收入预测存在着非线性、不稳定性和多经济因素影响的复杂性,提出用最小二乘支持向量回归机的方法对广东省从化市的税收收入进行预测,并建立数学模型。由于模型中的参数C和σ²直接影响支持向量机的预测效果,因此巧妙地融合了粒子群优化算法的思想,采用粒子群算法对参数进行寻优来确保预测模型的精确性和稳定性。仿真实验结果表明,相对于各参比模型,用粒子群算法对参数进行寻优的最小二乘支持向量回归机的预测精度有了显著提高,从而说明了该模型的有效性和实用性。

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

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.