神经网络扩展滤波算法及其多元光谱分辨应用  

Extended Kalman Filtering Tranined Neural Networks and its Applications to Multicomponent Spectral Resolution

作　　者：李志良[1] 曾鸽鸣[1] 梁本熹[1] 邱细敏[1] 胡芳[1] 李梦龙[2] 

机构地区：[1]湖南大学化学化工系,长沙410082 [2]四川联合大学化学系,成都610064

出　　处：《化学学报》1996年第10期1009-1015,共7页Acta Chimica Sinica

基　　金：国家自然科学基金;国家教委;机械部及日本文部省资助课题

摘　　要：前馈神经网络(NN)误差反向传播算法(BP)应用较广,但收敛较慢且易陷入局部极优,针对这一不足,本文提出了一种基于扩展滤波的快速学习新颖算法(EF).与BP相比较,该法不仅具有学习效率高,收敛速度快,所需学习次数少,数值稳定性好等优点,而且所需调节参数少.由非线性系统建模与辨识的模拟结果表明,EF是一种有效的神经学习新算法.该法用于多元光谱校正与分辨,获得良好结果.Backpropagation (BP), one of the most useful algorithms to train neural networks (NN), has some deficiencies and/or inadequacies such as low convergence and local optima. A novel learning method for training NN,extended Kalman filtering(EF), has been develped with rapid convergence speed, few iteration cycles and small hidden neurons. This EFNN method was used for multicomponent spectral resolution and simultaneous determination of composite pharmaceutical APC preparations and mixed aromatic species samples with satisfactory results.

关 键 词：神经网络 扩展滤波 反传算法 多元校正 光谱分辨 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O657.3[自动化与计算机技术—控制科学与工程]