Application of high-order neural networks in chemistry

Abstract

The effectiveness and usefulness of the so-called high-order neural networks for classification of chemical objects is demonstrated. The high-order neural networks usually do not need hidden neurons for correct interpretation of patterns. A simple formula for partial derivatives of the minimized objective (error) function is derived, which is used for production of weight coefficients during the adaptation process. An illustrative example dealing with inductive and resonance effects of functional groups by the second-order neural network is presented.