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Predictive Modular Neural Networks - Applications to Time Series (Paperback, Softcover reprint of the original 1st ed. 1998)
Loot Price: R2,663
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Predictive Modular Neural Networks - Applications to Time Series (Paperback, Softcover reprint of the original 1st ed. 1998)
Series: The Springer International Series in Engineering and Computer Science, 466
Expected to ship within 18 - 22 working days
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The subject of this book is predictive modular neural networks and
their ap plication to time series problems: classification,
prediction and identification. The intended audience is researchers
and graduate students in the fields of neural networks, computer
science, statistical pattern recognition, statistics, control
theory and econometrics. Biologists, neurophysiologists and medical
engineers may also find this book interesting. In the last decade
the neural networks community has shown intense interest in both
modular methods and time series problems. Similar interest has been
expressed for many years in other fields as well, most notably in
statistics, control theory, econometrics etc. There is a
considerable overlap (not always recognized) of ideas and methods
between these fields. Modular neural networks come by many other
names, for instance multiple models, local models and mixtures of
experts. The basic idea is to independently develop several
"subnetworks" (modules), which may perform the same or re lated
tasks, and then use an "appropriate" method for combining the
outputs of the subnetworks. Some of the expected advantages of this
approach (when compared with the use of "lumped" or "monolithic"
networks) are: superior performance, reduced development time and
greater flexibility. For instance, if a module is removed from the
network and replaced by a new module (which may perform the same
task more efficiently), it should not be necessary to retrain the
aggregate network."
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