Rapid optimization and minimal complexity in computational neural network multivariate calibration of chlorinated hydrocarbons using Raman spectroscopy

Improvements in the computational neural network modeling process are descr ibed with the goals of enhancing the optimization process and reducing NN m odel complexity. Improvements to the optimization process not only speed co mputation, but also can enhance the quality of the result. Complex NN model s require more intensive optimization procedures and are considerably more difficult to interpret. Performance of these new algorithms is demonstrated by results from training neural networks to quantitate composition of mixt ures of chlorinated hydrocarbons based on their Raman spectra. Copyright (C ) 2000 John Wiley & Sons, Ltd.