Hans Peter Bernhard, Walter Reinisch, Wolfgang Bauer,
"The Mutual Information Function as a Preprocessing Tool for Predictor Design"
: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), September 23-15, 1998, Vienna, Austria, Seite(n) 868-873, 1998
Original Titel:
The Mutual Information Function as a Preprocessing Tool for Predictor Design
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), September 23-15, 1998, Vienna, Austria
Original Kurzfassung:
In this work we present the application of the mutual information function to
the problem of predictor design. From our point of view optimum predictor
design depends not just on proper system identification, optimal fitting of
system parameters or appropriate methods to learn the system
behavior. We argue that it is most important to find the optimal input
parameters independent of the system identification or learning task. We
apply the mutual information function as a preprocessing tool to select,
in a least square sense, the best input parameters apriori. This means that
we use the data resulting from the data acquisition process to find the
combination of input parameter that is most ''informative'' on the output
to be predicted. As we will explain, this task is done using the
mutual information function, which serves as base for the upper bound on
the prediction gain. As a demonsration example,
we use load prediction for power distribution
networks.