A New Scheme of Nonlinear Continuous System Identification Using Radial Basis Function Networks

抄録

The nonlinear continuous system identification using radial basis function networks is studied in this paper. A learning law to adjust the network weight parameters and to suppress the effect of the modeling error is provided. The convergence of the learning law and the boundedness of adjusted parameters are proven based on the Lyapunov stability theorem. The results of simulations for a two-degree-of-freedom manipulator moving in a vertical plain show the effectiveness of the proposed identification scheme.