Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems
This is an incremental review and comparison for researchers in dynamic systems control.
The paper reviews applications of Generalized Regression Neural Networks (GRNN) in system identification and control, and compares GRNN with back-propagation neural networks, finding that GRNN has shorter training time and higher accuracy.
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.