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改进的粒子群算法的小波支持向量机预警模型
引用本文:苗旭东,魏连鑫.改进的粒子群算法的小波支持向量机预警模型[J].上海理工大学学报,2018,40(3):211-216.
作者姓名:苗旭东  魏连鑫
作者单位:上海理工大学理学院
基金项目:国家自然科学基金资助项目(11301340)
摘    要: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。

关 键 词:小波核函数  支持向量机  粒子群优化算法
收稿时间:2017/8/22 0:00:00

Improved Particle Swarm Optimization Parameters for Wavelet Support Vector Machine Early Warning Model
MIAO Xudong and WEI Lianxin.Improved Particle Swarm Optimization Parameters for Wavelet Support Vector Machine Early Warning Model[J].Journal of University of Shanghai For Science and Technology,2018,40(3):211-216.
Authors:MIAO Xudong and WEI Lianxin
Affiliation:College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China and College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
Abstract: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.
Keywords:wavelet kernel function  support vector machine  particle swarm optimization algorithm
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