Back-propagation neural network applications

Z. Ramadan, P. K. Hopke, M. J. Johnson and K. M. Scow, Application of PLS and back-propagation neural networks for the estimation of soil properties, Chemom. Intell. Lab. Syst., 75(1), 2005, 23-30. 

In this research, an application of artificial neural network in prediction of the rubber o-ring shrinkage in compression moulding is presented. A back propagation neural network was developed to determine the shrinkage based on amount of sulphur, the amount of carbon black, the mould temperature, an inside diameter and a cross sectional diameter. The neural network prediction for an inside diameter shrinkage and a cross sectional diameter shrinkage indicate that architectures 5-11-21-1 and 5-11-16-1 provide an optimized prediction within 95.9% and 96.1% accuracy, respectively. 

To understand neural networks, especially Kohonen, counter-propagation and back-propagation networks, and their applications... 

To optimize the neural network design, important choices must be made for the selection of numerous parameters. Many of these are internal parameters that need to be tuned with the help of experimental results and experience with the specific application under study. The following discussion focuses on back-propagation design choices for the learning rate, momentum term, activation function, error function, initial weights, and termination condition. 

Neural networks are also being seriously explored for certain classes of optimization applications. These employ parallel solution techniques which are patterned after the way the human brain functions. Statistical routines and back propagation algorithms are used to force closure on a set of cross linked circuits (equations). Weighting functions are applied at each of the intersections. 

The first application of a neural network in NMR was proposed by Thomsen and Meyer who analyzed one-dimensional spectra of simple molecules before application to complex oligosaccharides. Kjter and Poulsen - showed that the center of COSY cross-peaks can be found using neural networks. Their implementation consists of a feed-forward three-layer with 256 inputs programmed using a back-propagation error algorithm. As shown by Come et NOESY... 

Within the field of chemistry, various applications have already been published. Jansson " and Zupan and Gasteiger published an overview of an MLP (multilayer perceptron), that is trained by back-propagation of errors, and other types of neural networks. However, the basic source in this field continues to be the well-known book of Zupan and Gasteiger." In analytical chemistry, neural networks have been applied to pattern recognition, modeling, and prediction, for example, in multicomponent analysis or process control, to classification, clustering and pattern association. 

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