Global estimation of feedforward networks with a priori constraints

Abstract

Abstract. This paper presents a feedforward network estimation algorithm that addresses two issues, (i) avoiding local inferior minima to the performance criteria, and (ii) imposing a priori constraints to improve generalization and test economic hypotheses. The algorithm combines methods either previously developed or obviously beneficial but not yet combined. These involve combining linear least squares with simulated annealing, along with weight space reducing methods, to considerably improve its speed relative to pure simulated annealing. We present evidence on the algorithm's reliability at finding a global minimum. We also demonstrate how to constrain the estimation process to find networks that satisfy a given a priori condition. We provide examples of imposing a priori information to (i) prevent the trained network from making fundamental errors and (ii) to test economically interesting hypotheses.