  This paper depicts a brief revision of Generalized Regression Neural Networks
(GRNN) applications in system identification and control of dynamic systems. In
addition, a comparison study between the performance of back-propagation neural
networks and GRNN is presented for system identification problems. The results
of the comparison confirm that GRNN has shorter training time and higher
accuracy than the counterpart back-propagation neural networks.
