Using artificial neural networks for solving chemical problems Part I. Multi-layer feed-forward networks

Abstract Smits, J.R.M., Melssen, W.J., Buydens, L.M.C. and Kateman, G., 1994. Using artificial neural networks for solving chemical problems. Part I. Multi-layer feed-forward networks. Chemometrics and Intelligent Laboratory Systems , 22: 165–189. This tutorial focuses on the practical issues concerning applications of different types of neural networks. The tutorial is divided into two parts. In the first part, an overview of the general appearance of neural networks is given and the multi-layer feed-forward neural network is described. In the second part, the Kohonen self-organising feature map and the Hopfield network are discussed. Since the multi-layer feed-forward neural network is one of the most popular networks, the theory concerning this network can easily be found in other references (B.J. Wythoff, Chemom. Intell. Lab. Syst. , 18 (1993) 115–155) and is therefore only described superficially in this paper. Much attention is paid to the practical issues concerning applications of the networks. For each network, a description is given of the types of problems which can be tackled by the specific neural network, followed by a protocol for the development of the system. It is seen that different neural networks are suited for different kinds of problems. Application of the networks is not always straightforward; a lot of constraints and conditions have to be fulfilled when using neural networks properly. They appear to be powerful techniques, but often a lot of experience is needed. In this paper some guidelines are given to avoid the most common difficulties in applying neural networks to chemical problems.

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