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 developed 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.

Key words: NEURAL NETWORK