CAJ | 학술논문

作者针对X荧光分析中散射峰干扰和直线拟合精度差等问题，采用最优线性联想记忆(OLAM)神经网络方法解谱，研究了无散射峰标准谱的产生和本底干扰问题.通过用一维高斯分布随机数模拟本底计数产生无散射峰标准谱的方法，提高了分析精度.
작자침대X형광분석중산사봉간우화직선의합정도차등문제，채용최우선성련상기억(OLAM)신경망락방법해보，연구료무산사봉표준보적산생화본저간우문제.통과용일유고사분포수궤수모의본저계수산생무산사봉표준보적방법，제고료분석정도.
The methods of OLAM neural network have been introduced into the analysis of the hair XRF analysis to overcome the problems such as the disturbance of scattering peaks and the poor accuracy of linear fit of data. Simulating the background spectnm with one-dimension Gauss random numbers a method of producing the standard spectra without the scattering peaks is presented for the use of OLAM neural network techniques. The accuracy of measurement of hair XRF analysis is improved and the good measurement results are given.