Quantitative fuzzy neural network for analytical determination Original Research Article

A quantitative fuzzy neural network (Q-FNN) for pattern recognition in analytical determination is reported in this paper. The fuzzy neural network (FNN) combines a fuzzy logic system with an artificial neural network (ANN) so that it has both advantages of a high training speed and strong anti-interference. Importantly, the analytical concept of relative error (RE) in quantitative determination has been integrated into FNN so that the Q-FNN provides a very good quantitative capability in chemical analysis, and prevents the system from an over-fitting problem. The logarithm curve with noise in terms of analytical response versus concentration is calibrated by trained FNN and a close approximation to the ideal one without noise is obtained. The Q-FNN has been applied to the concentration determination of freon in the presence of interference gases. The prediction error for a test set in quantification is less than 10% while no qualitative mistake is observed, implying that the quantitative FNN has sustained the feature of pattern recognition. The results indicate that the Q-FNN has obvious advantages not only in converging speed, but also in the quantitative accuracy over the ANN.