Design of Intelligent Control System Based on General Regression Neural Network Algorithm

Abstract

this study discuss about the General Regression
Neural Network (GRNN) implementation for control system
application. Nowadays, hybrid theory tends to investigate within
control system research. Previously, many kinds of neural
network control schemes have been deployed. The problem was
related to the optimization in training phase to satisfy the
response system result based on plant’s identification phase.
GRNN was known as one passing learning algorithm with a
highly parallel structure. The algorithmic form can be used for
any regression problem in which the assumption of linearity data
is not satisfied related on performance problem. This
investigation using GRNN as the online dynamics learning model
can be use as predictor or estimator for control signal with the
performance reaches 99% to prove that GRNN is reliable as one
of modern Intelligent Control System.