Nonlinear error mapping technology in system identification

In system engineering, a growing interest in identification of nonlinear systems is induced by the fact that identification allows one to find the models for consecutive analysis and control. This paper studies the parameter identification features for nonlinear multivariable systems and reports results in the convergence analysis of the proposed identification algorithm. To handle the nonlinear identification, an innovative nonlinear mapping concept is offered, and Lyapunov's theory plays a key role in analysis of parameter convergence. The explored method significantly departures from linear error mapping philosophy, and the offered framework ensures parameter convergence and enhances the sets of initial parameter values where identification can be performed.