Selection of Training Data for Locally Recurrent Neural Network View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Krzysztof Patan , Maciej Patan

ABSTRACT

Artificial neural networks of the dynamic type provide an excellent mathematical tool for dealing with non-linear dynamic problems. There are many application domains where the accurate model of a process/plant plays key role. One of the most stimulating practical examples is Fault Detection and Identification (FDI) of industrial systems [1]. Preparation of experimental conditions in order to collect informative measurements can be very expensive and the data acquired form real-world system may be also very noisy, therefore using all the available data may lead to significant systematic modelling errors. More... »

PAGES

134-137

Book

TITLE

Artificial Neural Networks – ICANN 2010

ISBN

978-3-642-15821-6
978-3-642-15822-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15822-3_16

DOI

http://dx.doi.org/10.1007/978-3-642-15822-3_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001831603


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