Specific process models derived from extremely small data sets and general process models

Abstract

Definition of a needed particular process model is based on a combination of weighted known general process models and standard error minimization. The known general process models correspond to the biological processes of growing. The standard error is computed using new data and an ensemble of generated models. General models are based on polynomial functions and neural networks. Applications of polynomial functions of second, third and fourth degrees is analyzed. Supervised learning of the neural networks is based on the Levenberg-Marquardt algorithm. A very brief comment on the Vapnik-Chervonenkis dimension as an important parameter in modern learning theory, is also done in view of the analyzed cases.