An approach for the identification of nonlinear, dynamic processes with Kalman Filter-trained recurrent neural structures.
F. Heister, и R. Müller. Technical Report, 229. Department of Computer Science, (мая 1999)
Аннотация
In this article we demonstrate the identification of a nonlinear,
dynamic process with recurrent neural structures. The employed
network-structure is a Recurrent Multilayer Perceptron (RMLP), which
combines feedforward-- and recurrent architectures. We will show that
RMLPs are capable of learning the temporal behavior and characteristic
of an arbitrary, nonlinear, dynamic process. Apart from conventional
gradient-based algorithms, a sophisticated statistical method has been
considered for this challenging task - Global Extended Kalman Filtering
(GEKF). This powerful algorithm yields neural structures with a
significantly better performance, compared to conventional
gradient-based approaches. The new element in this work is the
application of the GEKF-Algorithm for recurrent neural structures,
which are employed in the identification of nonlinear, dynamic
processes. In order to supervise the quality of network-training,
appropriate performance-indexes for neural identification are
introduced. The distribution of the Moving Average Squared Error (MASE)
is employed as an objective optimality-criterion, in order to survey
the actual performance of recurrent neural structures during training.
%0 Report
%1 TR229
%A Heister, Frank
%A Müller, Rainer
%C Department of Computer Science
%D 1999
%K myown
%N 229
%T An approach for the identification of nonlinear, dynamic processes with Kalman Filter-trained recurrent neural structures.
%U http://www-info3.informatik.uni-wuerzburg.de/TR/tr229.pdf
%X In this article we demonstrate the identification of a nonlinear,
dynamic process with recurrent neural structures. The employed
network-structure is a Recurrent Multilayer Perceptron (RMLP), which
combines feedforward-- and recurrent architectures. We will show that
RMLPs are capable of learning the temporal behavior and characteristic
of an arbitrary, nonlinear, dynamic process. Apart from conventional
gradient-based algorithms, a sophisticated statistical method has been
considered for this challenging task - Global Extended Kalman Filtering
(GEKF). This powerful algorithm yields neural structures with a
significantly better performance, compared to conventional
gradient-based approaches. The new element in this work is the
application of the GEKF-Algorithm for recurrent neural structures,
which are employed in the identification of nonlinear, dynamic
processes. In order to supervise the quality of network-training,
appropriate performance-indexes for neural identification are
introduced. The distribution of the Moving Average Squared Error (MASE)
is employed as an objective optimality-criterion, in order to survey
the actual performance of recurrent neural structures during training.
@techreport{TR229,
abstract = { In this article we demonstrate the identification of a nonlinear,
dynamic process with recurrent neural structures. The employed
network-structure is a Recurrent Multilayer Perceptron (RMLP), which
combines feedforward-- and recurrent architectures. We will show that
RMLPs are capable of learning the temporal behavior and characteristic
of an arbitrary, nonlinear, dynamic process. Apart from conventional
gradient-based algorithms, a sophisticated statistical method has been
considered for this challenging task - Global Extended Kalman Filtering
(GEKF). This powerful algorithm yields neural structures with a
significantly better performance, compared to conventional
gradient-based approaches. The new element in this work is the
application of the GEKF-Algorithm for recurrent neural structures,
which are employed in the identification of nonlinear, dynamic
processes. In order to supervise the quality of network-training,
appropriate performance-indexes for neural identification are
introduced. The distribution of the Moving Average Squared Error (MASE)
is employed as an objective optimality-criterion, in order to survey
the actual performance of recurrent neural structures during training.
},
added-at = {2015-06-18T10:00:28.000+0200},
address = {Department of Computer Science},
author = {Heister, Frank and Müller, Rainer},
biburl = {https://www.bibsonomy.org/bibtex/247d0f4bf4e55c3f578f8fb5223e0ae30/trcsuniwue},
interhash = {693dd17e0c047cadacb8a3430b664d14},
intrahash = {47d0f4bf4e55c3f578f8fb5223e0ae30},
keywords = {myown},
month = may,
number = 229,
timestamp = {2015-06-18T10:00:28.000+0200},
title = {An approach for the identification of nonlinear, dynamic processes with Kalman Filter-trained recurrent neural structures.},
type = {Technical Report},
url = {http://www-info3.informatik.uni-wuerzburg.de/TR/tr229.pdf},
year = 1999
}