摘要
混合高斯模型能够有效地拟合概率密度函数,常用的混合高斯概率密度模型参数估计方法是EM迭代算法,这种算法的缺点是估计精度过分依赖于初始值,而且不能估计模型阶数。基于遗传算法的K-means初始化EM算法可以同时估计模型阶数和参数。试验结果表明,该算法具有更好的聚类效果。
The probability density function can be efficiently fitted by Gaussian mixture model.EM iteration algorithm is one of popular algorithms for parameters estimation of Gaussian mixture probability density model.However,this method de-pends on initial parameters highly and can not estimate the orders of models.K-means initialization EM algorithm based on the genetic algorithms can estimate the orders and parameters of models.The simulation results indicate that this method has a good clustering ability.
出处
《现代电子技术》
2010年第15期102-103,106,共3页
Modern Electronics Technique
基金
国家自然科学基金资助项目(60971130)
