27-06-2012, 01:54 PM
System Identification based on neural networks
[attachment=25751]
Abstract
System Identification based on neural networks has become a very important field in research projects.
An attempt has been made to use these neural networks based on a simple back propagation algorithm,
with some modifications on input/output vectors, to track a moving object such as aircraft. Prediction
was also made on the aircraft position, one step ahead in real time.
Introduction
The capability of neural networks for approximating arbitrary input-output mappings give a simple way
to identify unknown dynamic functions in order to predict the needed output one step ahead or more. In
a tracking system, measured radar signals mostly have been mixed with additive white noise. In order to
filter out or minimize this measured noise on-line and to predict the aircraft position one step ahead, a
simple back propagation algorithm has been used.
The Problem of Training Neural Network Without Actual Training Target
The major problem when we train the neural network to become a "neural filter" that can minimize the
measurement error, is the target value we want to use to train the networks. In [3], the actual values that
are used to train the network are known. When the mean square error of the trained network reaches the
threshold value that was set by the user, it becomes a neural filter and has the same function as Kalman
filter.
[attachment=25751]
Abstract
System Identification based on neural networks has become a very important field in research projects.
An attempt has been made to use these neural networks based on a simple back propagation algorithm,
with some modifications on input/output vectors, to track a moving object such as aircraft. Prediction
was also made on the aircraft position, one step ahead in real time.
Introduction
The capability of neural networks for approximating arbitrary input-output mappings give a simple way
to identify unknown dynamic functions in order to predict the needed output one step ahead or more. In
a tracking system, measured radar signals mostly have been mixed with additive white noise. In order to
filter out or minimize this measured noise on-line and to predict the aircraft position one step ahead, a
simple back propagation algorithm has been used.
The Problem of Training Neural Network Without Actual Training Target
The major problem when we train the neural network to become a "neural filter" that can minimize the
measurement error, is the target value we want to use to train the networks. In [3], the actual values that
are used to train the network are known. When the mean square error of the trained network reaches the
threshold value that was set by the user, it becomes a neural filter and has the same function as Kalman
filter.