径向基函数神经网络用于重叠色谱峰解析

 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。

Resolution of Overlapping Chromatographic Peaks by Radial Basis Function Neural Network

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(sigma) 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.