Analog neural networks as asymptotically exact dynamic solvers

The paper deals with analog neural networks which can be used for solving nonlinear constrained optimization tasks using the penalty function approach. The neural model developed can be regarded as asymptotically exact dynamic solver in a sense that the equilibrium state represents a solution which can be arbitrarily close to that of the original constrained optimization task. Although it is a quite natural requirement, generally it can be fulfilled only with infinitely large penalty multipliers. The neural network presented provides another way for generating solutions arbitrarily close to the exact one at finite penalty multipliers. The usefulness of the optimization neural network presented is also illustrated by numerical examples.