In this paper we consider several Neural Network architectures for
solving nonlinear programming problems with inequality constrains. This is an extension of previous authors’ work and here we present a new architecture for convex programming problems. The architecture is based on alternativě pseudocost function, which do not require large penalty pararneter values. Simulation results based on SIMULINK® models are given and compared.