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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2013 - 1
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Automatic and Parallel Optimized Learning for Neural Networks performing MIMO Applications

FULGINEI, F. R. See more information about FULGINEI, F. R. on SCOPUS See more information about FULGINEI, F. R. on IEEExplore See more information about FULGINEI, F. R. on Web of Science, LAUDANI, A. See more information about  LAUDANI, A. on SCOPUS See more information about  LAUDANI, A. on SCOPUS See more information about LAUDANI, A. on Web of Science, SALVINI, A. See more information about  SALVINI, A. on SCOPUS See more information about  SALVINI, A. on SCOPUS See more information about SALVINI, A. on Web of Science, PARODI, M. See more information about PARODI, M. on SCOPUS See more information about PARODI, M. on SCOPUS See more information about PARODI, M. on Web of Science
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Author keywords
neural networks, multivariate function decomposition, learning optimization, parallel computing, genetic algorithms

References keywords
neural(23), networks(14), network(9), salvini(6), riganti(6), fulginei(6), decomposition(5), problems(4), optimization(4), feed(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-02-28
Volume 13, Issue 1, Year 2013, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.01001
Web of Science Accession Number: 000315768300001
SCOPUS ID: 84875323616

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An automatic and optimized approach based on multivariate functions decomposition is presented to face Multi-Input-Multi-Output (MIMO) applications by using Single-Input-Single-Output (SISO) feed-forward Neural Networks (NNs). Indeed, often the learning time and the computational costs are too large for an effective use of MIMO NNs. Since performing a MISO neural model by starting from a single MIMO NN is frequently adopted in literature, the proposed method introduces three other steps: 1) a further decomposition; 2) a learning optimization; 3) a parallel training to speed up the process. Starting from a MISO NN, a collection of SISO NNs can be obtained by means a multi-dimensional Single Value Decomposition (SVD). Then, a general approach for the learning optimization of SISO NNs is applied. It is based on the observation that the performances of SISO NNs improve in terms of generalization and robustness against noise under suitable learning conditions. Thus, each SISO NN is trained and optimized by using limited training data that allow a significant decrease of computational costs. Moreover, a parallel architecture can be easily implemented. Consequently, the presented approach allows to perform an automatic conversion of MIMO NN into a collection of parallel-optimized SISO NNs. Experimental results will be suitably shown.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 1,354 TCR
SCOPUS® Citations for all references: 5,644 TCR

Web of Science® Average Citations per reference: 47 ACR
SCOPUS® Average Citations per reference: 195 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-01-13 14:29 in 188 seconds.

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