This paper deals with an optimal method concerning nonlinear parameter identification of risk technical systems (automobile and railway transport, aircrafts, marine and river transport, chemical installations, munitions, information society suffering by terrorism). Unknown states of the model are built by sliding observers which converge in a finite time. Due to this property, it is possible to derive equations of the model in order to obtain an estimation law which converges to the nominal values of the parameters also in the finite time.
Real-coded genetic algorithms and nonlinear parameter identification
