Multivariate nonlinear modelling of fluorescence data by neural network with hidden node pruning algorithm

Abstract A hidden node pruning algorithm (HNPA) has been proposed as a method of configuration optimization and training in multilayer feedforward network. By this approach, a network initially bearing excessive hidden nodes is trained to sufficient precision and is pruned to the optimal size. Upon pruning, significant hidden nodes are determined by singular value decomposition (SVD) of output matrix of hidden layer and are retained. Weights and biases are preset intentionally in the pruned system, then training continues. The method has been tested with simulated nonlinear data and then applied to the modelling of a nonlinear fluorescence data of a real multicomponent analytical system, and satisfactory quantitative results have been achieved.