  Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system
identification problem. The proposed algorithm employs the concept of
time-space orthogonality and separately models the dynamics and nonlinear
complexities of the system. The proposed RBF architecture is explored for the
estimation of a highly nonlinear system and results are compared with the
standard architecture for both the conventional and fractional gradient
decent-based learning rules. The spatio-temporal RBF is shown to perform better
than the standard and fractional RBFNNs by achieving fast convergence and
significantly reduced estimation error.
