摘要
结合磨光法和最优化理论提出一种随机优化磨光算法(SOS算法),算法通过原始值的参数化和调整幅度的修改,利用优化理论优化控制点.实例表明,随机优化磨光算法比样条修正磨光法和灰色马尔可夫链预测模型精度要高得多;而且所得到的误差变化更稳定.
In this paper,a stochastic optimization smoothing algorithm（sos algorithm） is got through Combination of polishing method and optimization, the algorithm optimize controlled points by original data parameterized and adjusted range and optimization theory. The example express that the SOS algorithm is more high-precision than spline modified smoothing method and grey markov chain forecast model; Moreover,error range got is much stabler.
出处
《数学的实践与认识》
CSCD
北大核心
2010年第18期159-162,共4页
Mathematics in Practice and Theory
基金
江西省教育厅科研项目GJJ09270
关键词
灰色马尔可夫链
磨光法
原始值参数化
随机优化
grey markov chain
smoothing method
original data parameterized
stochastic optimization
