A pseudo-output error algorithm to improve global convergence

Abstract

In this work, a new off-line optimization approach is proposed to improve the global convergence. This algorithm, called pseudo-output error algorithm, is based on the introduction of a stationary filter in the sensitivity functions of the Newton algorithm. The convergence of the proposed algorithm is studied and analyzed. A comparison with Newton algorithm is performed to evaluate its performance. The results of the simulations show that the pseudo-output error algorithm converges to true parameters describing the system to be identified. Whereas, the Newton algorithm might converge to a secondary minimum for the same input-output data and initialized parameters.