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dengwei_19

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铁齿金不换诚实可靠小郎君

[求助] 求曲线拟合中简化方程,非常感谢

做曲线拟合,采用pseudo-voigt卷积高斯和洛伦兹函数,其实也不难,难就难在manual上给的是卷积后的pseudo-voigt,参数正是软件调试所需,程序不开源看不见,实在没办法就只能猜了。。。。。想用matlab进行拟合,但首先要做的事简化方程,实在太难,求高手简化方程,顺带写写代码更好 非常感谢。
this function is a combination of Gaussian and Lorentzian with weight coefficient between them. The Gaussian function models better the real experimental effects far on the wings of the central substrate peak, and pseudo-Voigt balances the wings and substrate peak spread. The convolution integral for general pseudo-Voigt function is eq.(1)

in eq. (1) A:weight coefficient; σ: dispersion of Gaussian function; and b is a Lorentzian factor.
The normalization factor is defined from eq.(2)

where G and L are Gaussian and Lorentzian, respectively. The convolutions for separate Gaussian and Lorentzian are given by corresponding parts of formulas above. The normalization used for these functions in ** defines the values of gaussian dispersion σ and Lorentzian factor b in the scanning axis units, i.e. degrees, radians or arcseconds for angular scan types and reciprocal nanometers for reciprocal scan types.


That's all the information I can present here, thank you
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dengwei_19

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引用回帖:
5楼: Originally posted by feixiaolin at 2013-12-07 16:49:29
http://en.wikipedia.org/wiki/Voigt_profile
Which parameters do you want?

A:weight coefficient; σ: dispersion of Gaussian function; and b is a Lorentzian factor.
That's all I  want ,  thank you
7楼2013-12-07 23:26:59
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feixiaolin

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Please change you pictures into *.jpg style and reupload, otherwise the pictures can not be seen.

[ Last edited by feixiaolin on 2013-12-7 at 16:20 ]
2楼2013-12-07 11:23:19
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dengwei_19

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铁齿金不换诚实可靠小郎君

公式在这里
求曲线拟合中简化方程,非常感谢
1.jpg


求曲线拟合中简化方程,非常感谢-1
2.jpg

3楼2013-12-07 11:54:07
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dengwei_19

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铁齿金不换诚实可靠小郎君

feixiaolin: 编辑内容 2013-12-07 16:20
引用回帖:
2楼: Originally posted by feixiaolin at 2013-12-07 11:23:19
Please change you pictures into *.jpg style and reupload, otherwise the pictures can not be seen.

已上传

[ Last edited by feixiaolin on 2013-12-7 at 16:20 ]
4楼2013-12-07 11:57:54
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