Abstract
A new approach based on the decomposition, in position-time space, of operating functions (OFs) is proposed and various criteria of parameter identification like the output least squares (OLS) and the regularized least squares (RLS) are formulated and incorporated in the framework. Several optimization algorithms such as the Gauss-Newton, conjugate gradient and Levenberg-Marquardt are diligently modified and computed in order to search the performed algorithm to identify, in space and time, the numerical fields of the model parameters. Because the sensitivity of the criteria to the design variables plays a very important role in the optimization problem, various techniques such as finite difference method and direct differentiation method are tested. The enhanced performance of the model with the newly identified OFs is proved by a higher conformity of its predictions with the real data of drying wood systems (DWSs).