Counterpropagation networks applied to the classification of alkanes through infrared spectra

Abstract

Enhanced uni-flow counterpropagation networks are used as pattern recognition systems and applied to the identification of chemical structure from corresponding infrared spectra. It is shown that such networks are more suitable for this type of problem than backpropagation networks, both in terms of training times and network performance. The problem of optimum classification between highly similar infrared spectra is addressed, and factors such as training set size, sampling rate, data pre-processing, output data representation and the number of Kohonen layer nodes are considered in this context. It is shown that such networks may achieve rates of correct classification in excess of 90%, although the learning of correct decision boundaries is highly sensitive to the above parameters in cases where the non-informational content of training and test data varies considerably with respect to the informational content, and hence clustering of classes in pattern space is incomplete.