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首页> 外文期刊>Journal of liquid chromatography and related technologies >Radial basis function networks in liquid chromatography: improved structure-retention relationships compared to principal components regression (PCR) and nonlinear partial least squares regression (PLS)
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Radial basis function networks in liquid chromatography: improved structure-retention relationships compared to principal components regression (PCR) and nonlinear partial least squares regression (PLS)

机译：液相色谱法中的径向基函数网络：与主成分回归（PCR）和非线性偏最小二乘回归（PLS）相比，结构保留关系得到改善

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摘要

The application of the second most popular artificial neural networks (ANN), namely the radial basis function networks, has been developed for obtaining sufficient quantitative structure-retention relationships (QSRR) with improved accuracy. The present study examined a dataset of 25 substances as solutes to two different stationary phases (silica and alumina). The solutes were analyzed to their structural descriptors and related to their retention behavior, as expressed by their capacity factors, using radial basis function (RBF) and generalized regression neural networks (GRNN) as function approximation systems. The proposed methods led to substantial gain in both the prediction ability and the computation speed of the resulting models compared to regression models. Furthermore, the results were compared with that produced from classical linear and nonlinear multivariate regression such as principal components regression (PCR) and nonlinear (polynomial) partial least squares regression (PLS). Some of the proposed ANN models diminished the number of outliers, during their implementation to unseen data (solutes), to zero.
机译：已经开发了第二流行的人工神经网络（ANN），即径向基函数网络的应用，以获取足够的定量结构保留关系（QSRR），并提高了准确性。本研究检查了25种物质的数据集，这些物质是两种固定相（二氧化硅和氧化铝）的溶质。使用径向基函数（RBF）和广义回归神经网络（GRNN）作为函数逼近系统，对溶质进行了结构描述，并与它们的保留行为（由容量因子表示）相关联。与回归模型相比，所提出的方法导致了所得模型的预测能力和计算速度的显着提高。此外，将结果与经典线性和非线性多元回归（例如主成分回归（PCR）和非线性（多项式）偏最小二乘回归（PLS））产生的结果进行了比较。某些拟议的ANN模型将对未见数据（溶质）的离群数减少为零。

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