Linear approximation model network and its formation via evolutionary computation

To overcome the deficiency of local model network (LMN) techniques, an alternative linear approximation model (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linearizing models fitted along operating trajectories, where the individual models are simply networked through output or parameter interpolation. The linearizing models are valid for the entire operating trajectory and hence overcome the local validity of LMN models. LAMs can be evolved from sampled step response data directly, eliminating the need for local linearization upon a pre-model using derivatives of the nonlinear system. Validation results show that the proposed method offers a simple, transparent and accurate multivariable nonlinear modeling technique.