基于改进排序遗传算法的径向基函数神经网络色谱峰解析

Resolution of Chromatographic Peaks by Radial Basis Function Neural Network Based on Plate Model Based on a Developed Sorting Genetic Algorithm

构造了以塔板模型为基函数的径向基函数神经网络 (P_RBFNN) ,为了使P_RBFNN具有结构重组能力 ,又在网络学习算法中引入了鲁棒 (Rubust)和随机全局最优的两阶段排序遗传算法 :结构学习和进化。P_RBFNN结合改进的排序遗传算法很适合组分数未知的色谱 (含重叠 )峰解析。

Radial Basis Function Neural Network Based on Rate Model (P-RBFNN) is constructed for resolution of chromatographic peaks of unknown components number. Then a two-phase sorting genetic algorithm (TP-SGA)training structure and evolving is intruduced to train the network so that it has the ability of re-constructed structure. TP-SGA has robustness and random globe optimization. The alternate use of gradient descent and TP-SGA makes the network have the ability to learn structure, therefore makes itself adaptable to resolution of the chromatographic peaks of unknown components number. The method proposed here needs no artificial interference, not only has it robustness and globalism. With its characteristics related above and its ability of decomposing and analysing, this method has obvious advantages comparing with others.