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摘 要:在高斯基径向基函数神经网络(RBFNN)学习算法中引入了鲁棒性和随机全局寻优的两阶段遗传算法:结构学习和参数优化。通过两阶段学习算法的交替使用,使网络具有结构自学习和参数优化的能力,而后将网络应用于组分数未知的重叠色谱峰解析。该方法具有不需人为干预,可自动确定网络结构即组分数的优点;并且解析精度较高,适用于多组分重叠色谱峰的解析;对完全重叠色谱峰也具有良好的解析能力。
Abstract:A
new algorithm-resolution of overlapping chromatographic peaks by radial
basis function neural network(RBFNN) is presented.
A two-phase genetic
algorithm(GA) which has robustness and random globe optimization is used
to train RBFNN so that it has the ability on the resolution of
overlapping chromatographic peaks. The
two-phase genetic algorithm involves two procedures: training structure
and optimizing parameter. The
first procedure uses GA to train the architectures of RBFNN, the second
procedure uses gradient descent to train the center(tR) and
the width(σ)
of RBFNN. The
alternate use of these two procedures makes the network having the
ability to learn structure, therefore makes itself adaptable to
resolution of the chromatographic peaks with unknown number of
components. The
method proposed here needs no artificial interference, not only has it
robustness and globalism, but also the ability of accurate resolution to
completely overlapped chromatographic peaks. The
simulation experiments show that this method is more accurate than other
methods. |
基金项目:辽宁省自然科学基金资助(编号为972147) 作者简介:李一波(1963-),男,副教授,现为东北大学在职博士研究生,E-mail:liyibo@yes100.com. 作者单位:李一波(沈阳航空工业学院,辽宁沈阳 110034) 黄小原(东北大学,辽宁沈阳 110006) 沙明(辽宁中医学院,辽宁沈阳 110032) 孟宪生(辽宁中医学院,辽宁沈阳 110032) 参考文献: [1]Sten O E. J Electroanal Chem,1990,296:371-394 |
原载于《色谱》2001 Vol.19 No.2
P.112-115
http://periodical.wanfangdata.com.cn/periodical/sp/sp2001/0101/0101ml.htm