Neural network methodology

General regression neural network methodology is potentially more useful for predicting the QSPR relationships as compared to multiple linear regressions. 

One of the most important applications of neural network methodology is in the extrapolation of electrochemical impedance data obtained in corrosion studies.34 Electrochemical impedance spectroscopy (EIS) can be used to obtain instantaneous corrosion rates. The validation of extension of EIS data frequency range, which is conventionally difficult, can be done using a neural network system. In addition to extension of impedance data frequency range, the neural network identifies problems such as the inherent variability of corrosion data and provides solutions to the problems. Furthermore, noisy or poor-quality data are dealt with by neural works through the output of the parameters variance and confidence.33... 

Rose, V. S., Croall, I. E, MacFie, H. J. H. An apphcation of unsupervised neural network methodology (Kohonen topology-preserving mapping) to QSAR analysis. Quant. Struct.-Act. Relat. 1991,10,6-15. 

V Venkatasubramanian and K Chan. A neural network methodology for process fault diagnosis. AIChE J., 35(12) 1993-2002, 1989. 

Alonso et al. (2011PCCP20564) have successfully applied the neural network methodology in the study of the substituent effect on aromaticity for a set of pyrimidine derivatives with a potential push-pull character. The interplay between aromaticity, planarity, steric effect, and charge transfer properties of all substituted pyrimidine derivatives has been also discussed (2011PCCP20564). 

Dreyfus, G., 2005. Neural networks an overview, fit Dreyfus, G. (Ed.), Neural Networks— Methodology and Apphcations. Springer, Heidelberg. 

A more recently introduced technique, at least in the field of chemometrics, is the use of neural networks. The methodology will be described in detail in Chapter 44. In this chapter, we will only give a short and very introductory description to be able to contrast the technique with the others described earlier. A typical artificial neuron is shown in Fig. 33.19. The isolated neuron of this figure performs a two-stage process to transform a set of inputs in a response or output. In a pattern recognition context, these inputs would be the values for the variables (in this example, limited to only 2, X and x- and the response would be a class variable, for instance y = 1 for class K and y = 0 for class L. 

The search for optima within a given experimental space can also be realized by methodologies different from those that we have discussed before. We want to highlight two of them in this context, namely genetic algorithms and neural networks. 

R. Feraud and F. Clerot, A methodology to explain neural network classification, Neural Networks, 15(2), 2002, 237-246. 

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