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Finite-Time Normal Mode Disturbances and Error Growth During Southern Hemisphere Blocking
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作者 Jorgen S.FREDERIKSEN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2005年第1期69-89,共21页
The structural organization of initially random errors evolving in abarotropic tangent linear model, with time-dependent basic states taken from analyses, is examinedfor cases of block development, maturation and deca... The structural organization of initially random errors evolving in abarotropic tangent linear model, with time-dependent basic states taken from analyses, is examinedfor cases of block development, maturation and decay in the Southern Hemisphere atmosphere duringApril, November, and December 1989. The statistics of 100 evolved errors are studied for six-dayperiods and compared with the growth and structures of fast growing normal modes and finite-timenormal modes (FTNMs). The amplification factors of most initially random errors are slightly lessthan those of the fastest growing FTNM for the same time interval. During their evolution, thestandard deviations of the error fields become concentrated in the regions of rapid dynamicaldevelopment, particularly associated with developing and decaying blocks. We have calculatedprobability distributions and the mean and standard deviations of pattern correlations between eachof the 100 evolved error fields and the five fastest growing FTNMs for the same time interval. Themean of the largest pattern correlation, taken over the five fastest growing FTNMs, increases withincreasing time interval to a value close to 0.6 or larger after six days. FTNM 1 generally, but notalways, gives the largest mean pattern correlation with error fields. Corresponding patterncorrelations with the fast growing normal modes of the instantaneous basic state flow aresignificant' but lower than with FTNMs. Mean pattern correlations with fast growing FTNMs increasefurther when the time interval is increased beyond six days. 展开更多
关键词 normal modes finite-time normal modes BLOCKING tangent linear model pattern correlations
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使用自动微分的分类算法
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作者 张海斌 王美 《北京工业大学学报》 CAS CSCD 北大核心 2007年第6期656-659,共4页
解决支持向量机中的分类算法需要计算多变量函数的有关偏导数问题,通常使用的计算方法符号微分和差分近似.对于中大规模问题来说,使用符号微分方法,成本昂贵,有时甚至不可行,在计算导数的方向梯度时,利用差分方法虽然可以降低计算成本... 解决支持向量机中的分类算法需要计算多变量函数的有关偏导数问题,通常使用的计算方法符号微分和差分近似.对于中大规模问题来说,使用符号微分方法,成本昂贵,有时甚至不可行,在计算导数的方向梯度时,利用差分方法虽然可以降低计算成本,但得到的是近似值,而且确定恰当的差分区间也很困难.本文将自动微分技术与分类算法相结合,以较低的成本精确计算了中大规模问题函数的导数,建立并研究了使用自动微分的分类算法.并用数值试验验证了这一算法的有效性. 展开更多
关键词 数据挖掘 支持向量机 牛顿法 自动微分 切线性模式 伴随模式
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