Parameter estimation using linear programming and quasilinearization

Abstract

Abstract A generalized algorithm for the estimation of parameters in a process model from experimental data is presented in this paper. The algorithm, which combines linear programming with quasilinearization, is formulated and its advantages and limitations are discussed. Examples are included to illustrate application of the algorithm to real engineering problems, one of which was encountered in industry and another of which was encountered in a control study. The examples demonstrate the incorporation of constraints, convergence promotion and reliability estimates into the identification.