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Extended Kalman Filtering Algorithm Trained Neural Networks and Its Application to the Multicomponent Pharmaceutical Spectrophotometric Analysis

Xia Zhining Li Zhiliang Matsumoto S.(Department of Applied Chamistry,Chongqing University)  
Traditional error backpropagation (BP) algorithm for implementing the artificial neural networks (NN) has the drawback of slow convergence speed and the possibility of being stuck into the local optima. The NN learning process can be treated as a nonlinear state/parameter estimation problem, i.e. the adjustment of the mode weights under given input/output conditions. Extended Kalman filtering (EF) as a nonlinear state/parameter estimation tool has been introduced into the NN algorithm for implementing the learning process cycles and the EF has shown a relatively high convergence speed. The proposed method has been used for multicomponent spectrophotometric analysis for drug assay and/or pharmaceutical practice, with satisfactory results.
【Fund】: 国家自然科学基金 国家教委留学回国人员启动基金 国家教委优秀年轻教师基金
【CateGory Index】: O657.32
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