Ni, XF and Simons, SJR (1996) Nonlinear dynamic system identification using radial basis function networks. In: PROCEEDINGS OF THE 35TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4. (pp. 935 - 936). I E E E
Full text not available from this repository.
A radial basis function (RBF) network is used to approximate a continuous nonlinear dynamic system described by a set of nonlinear state equations. The learning laws to adjust the network weight parameters are derived using a Lyapunov function, such that the convergence is guaranteed. Simulations show that the method is simple and extremely effective. The robustness of the approach, with respect to the parameters and implementation, is considered in the paper.
|Title:||Nonlinear dynamic system identification using radial basis function networks|
|Event:||35th IEEE Conference on Decision and Control|
|Dates:||1996-12-11 - 1996-12-13|
|UCL classification:||UCL > VP International > UCL Australia|
Archive Staff Only: edit this record