Instrumental variable methods for identifying partial differential equation models
Abstract
This paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input-output data. The proposed method is compared with conventional least-squares and other instrumental variable-based techniques. Monte Carlo simulation analysis results are presented to illustrate the effectiveness and superiority of the proposed method in the presence of additive output measurement noise and under different spatio-temporal sampling conditions.