Estimation of parameters of kinetic compartmental models by use of computational neural networks

A novel methodological approach to the estimation of parameters involved in multicomponent kinetic determinations from real kinetic data by use of computational or artificial neural networks (ANNs) is proposed. The ANN input data used are also estimates obtained by using the Levenberg-Marquardt method in the form of an approximate nonlinear function that is the sum of the two expressions associated with the pseudo-first-order kinetics of the two mixture components. The performance of the optimized network architecture, 2:4s:21, was tested at variable rate constant ratios. The reduced dimensions of the network input space obtained using the Kolmogorov-Sprecher theorem result in improved limits of precision in estimating parameters at near-unity rate constant ratios. Experiments with real kinetic data provided a relative standard error of prediction of 2.47% and 4.23% for the two mixture components. These errors are much smaller than those obtained with existing alternative methods, particularly at the low rate constant ratio involved (1.37).