Generalized constraint neural network regression model subject to equality function constraints

Abstract

This paper describes a progress of the previous study on the generalized constraint neural networks (GCNN). The GCNN model aims to utilize any type of priors in an explicate form so that the model can achieve improved performance and better transparency. A specific type of priors, that is, equality function constraints, is investigated in this work. When the existing approaches impose the constrains in a discretized means on the given function, our approach, called GCNN-EF, is able to satisfy the constrain perfectly and completely on the equation. We realize GCNN-EF by a weighted combination of the output of the conventional radial basis function neural network (RBFNN) and the output expressed by the constraints. Numerical studies are conducted on three synthetic data sets in comparing with other existing approaches. Simulation results demonstrate the benefit and efficiency using GCNN-EF.