改进的粒子群算法的小波支持向量机预警模型

Improved Particle Swarm Optimization Parameters for Wavelet Support Vector Machine Early Warning Model

将小波函数引入支持向量机核函数,同时在支持向量机的学习算法上,引入了改进的粒子群优化算法,使得支持向量机的参数得到最优解,从而建立上市公司财务困境预警模型。实验结果表明,本文提出方法的预测准确率高于普通的小波支持向量机预警模型。

The wavelet function was introduced into the support vector machine kernel function. At the same time, an improved particle swarm optimization algorithm was introduced in the learning algorithm of the support vector machine, so that the optimal solution of support vector machine parameters could be provided to establish a financial distress early warning model for listed companies. The results show that the proposed method is more accurate than the ordinary wavelet support vector machine early warning model.