Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems

TL;DR

This paper reviews the use of Generalized Regression Neural Networks (GRNN) in dynamic system identification and control, highlighting their advantages over back-propagation neural networks in training speed and accuracy.

Contribution

It provides a comparative analysis between GRNN and back-propagation neural networks, demonstrating the superior performance of GRNN in system identification tasks.

Findings

GRNN has shorter training time than back-propagation neural networks.

GRNN achieves higher accuracy in system identification.

The review summarizes key applications of GRNN in control systems.

Abstract

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.