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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7-8/2013

01.06.2013 | Original Article

Some refinements of the standard autoassociative neural network

verfasst von: Behrooz Makki, Mona Noori Hosseini

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

Improving the training algorithm, determining near-optimal number of nonlinear principal components (NLPCs), extracting meaningful NLPCs, and increasing the nonlinear, dynamic, and selective processing capability of the standard autoassociative neural network are the objectives of this article that are achieved independently by some new refinements of the network structure and the training algorithm. In addition, three different topologies of the network are presented, which make it possible to perform local nonlinear principal component analysis. Performances of all methods are evaluated by a stock price database that demonstrates their efficiency in different situations. Finally, as it will be illustrated in the last section, the proposed structures can be easily combined together, which introduce them as efficient tools in a wide range of signal processing applications.
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Metadaten
Titel
Some refinements of the standard autoassociative neural network
verfasst von
Behrooz Makki
Mona Noori Hosseini
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0825-5

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