Interpretation of infrared spectra with modular neural-network systems

Abstract The interpretation of infrared (IR) spectra is not straightforward and requires much time and expertise. In this study the interpretation of IR spectra using artificial neural networks is addressed. The conventional approach is to design a single neural network to cover the problem domain. In this study a different approach is taken by tackling specific sub-problems with small, dedicated neural networks. Such networks are intended to form the modules of a larger, structured system for spectrum interpretation. The problem domain chosen in this preliminary work concerned the decision on the presence or absence of various functional groups (i.e., alcohols and carbonyls). Several modules were created, as well as a large, ‘flat’ neural network which covered the entire problem domain. The performance of the specialized modules was compared with that of the flat network and with that of a genuine expert.

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