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1)  double-sine function
双正弦函数
1.
The maximum problem of double-sine function with constraints is studied based on genetic algorithm and the convergence for optimizing constrained double-sine function is analyzed,which is affected by those genetic algorithm parameters.
文章采用遗传算法,研究了受限双正弦函数的极大值问题,给出了遗传算法寻优后的结果,并就遗传算法参数对受限双正弦函数寻优的收敛性进行了分析,结果表明遗传算法准确且较快地得到了双正弦函数的极大值。
2)  hyperbolic sine function
双曲正弦函数
1.
An attempt to offer a new treatment of end point error in precipitation titration by using hyperbolic sine function is given in this paper.
基于沉淀滴定基本原理,利用双曲正弦函数性质提出了一种处理沉淀滴定终点误差的新方法。
3)  counter-hyperbolic sine function
反双曲正弦函数
1.
According to the conclusion that the smoothness level of a data row can be increased through the transform of the counter-hyperbolic sine function,two methods of autoregression forecasting based on the increasing time series are given.
依据"利用反双曲正弦函数变换提高数据列光滑程度"的结论,给出了两种异方差条件下递增时间序列自回归预测的改善方法。
2.
This paper proves that the smooth degree of a data row can be increased by transforming the counter-hyperbolic sine function.
证明了利用反双曲正弦函数变换能提高数据列的光滑程度,获得结论:设{x_(k)}为递增数据列,x_(1)>0,y(k)=ln(x_(k)+(x_(k)~2+1)~(1/2)),则数据列{y_(k)}比数据列{x_(k)}光滑。
4)  arc-hyperbolic sine function
反双曲正弦函数
1.
A new conclusion is put forward,in which the smooth degree of the data row can be enhanced by means of the arc-hyperbolic sine function transformation.
提出了"利用反双曲正弦函数变换提高数据列光滑程度"的新结论,获得了递增时间序列改善的自回归预测新方法。
2.
This paper show that the transformation using arc-hyperbolic sine function can smooth the data row.
证明了利用反双曲正弦函数变换能提高数据列的光滑程度,给出了改善的自回归预测方法,并且举例加以论证。
5)  hyperbolic sine facely
双曲正弦函数族
6)  sine function
正弦函数
1.
The Development of the Courseware of Applying Visual Basic Language to Simulating "Drawing Image of Sine Function with Sine Line";
利用Visual Basic语言模拟“用正弦线作正弦函数图象”的课件开发
2.
Draw Sine Function Image with Computer;
计算机绘制正弦函数图象
3.
On some identities of sine and cosine functions;
关于正弦函数和余弦函数的一些恒等式
补充资料:双曲正弦


双曲正弦
sine, hyperbolic

双曲正弦[疵,场,劝浦c;c“。”r,nep6o。。,ee心‘] 见双曲函数(h”erbolic frm ctions).
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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