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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (705 KB) | Citation | Downloads: 1,278 | Views: 4,289

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

Abstract
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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

Cited-By ISI Web of Science

Web of Science® Times Cited: 21 [View]
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Cited-By CrossRef

SCOPUS® Times Cited: 29
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Cited-By CrossRef

[1] On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review, Laudani, Antonino, Lozito, Gabriele Maria, Riganti Fulginei, Francesco, Salvini, Alessandro, Computational Intelligence and Neuroscience, ISSN 1687-5265, Issue , 2015.
Digital Object Identifier: 10.1155/2015/818243
[CrossRef]

[2] Two FPGA-Oriented High-Speed Irradiance Virtual Sensors for Photovoltaic Plants, Oliveri, Alberto, Cassottana, Luca, Laudani, Antonino, Riganti Fulginei, Francesco, Lozito, Gabriele Maria, Salvini, Alessandro, Storace, Marco, IEEE Transactions on Industrial Informatics, ISSN 1551-3203, Issue 1, Volume 13, 2017.
Digital Object Identifier: 10.1109/TII.2015.2462293
[CrossRef]

[3] A New Neural Predictor for ELF Magnetic Field Strength, Coco, Salvatore, Laudani, Antonino, Fulginei, Francesco Riganti, Salvini, Alessandro, IEEE Transactions on Magnetics, ISSN 0018-9464, Issue 2, Volume 50, 2014.
Digital Object Identifier: 10.1109/TMAG.2013.2283022
[CrossRef]

[4] A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting, KAMPOUROPOULOS, K., ANDRADE, F., GARCIA, A., ROMERAL, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 14, 2014.
Digital Object Identifier: 10.4316/AECE.2014.01002
[CrossRef] [Full text]

[5] SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds, Schmid, Maurizio, Riganti-Fulginei, Francesco, Bernabucci, Ivan, Laudani, Antonino, Bibbo, Daniele, Muscillo, Rossana, Salvini, Alessandro, Conforto, Silvia, Computational and Mathematical Methods in Medicine, ISSN 1748-670X, Issue , 2013.
Digital Object Identifier: 10.1155/2013/343084
[CrossRef]

[6] A Neural-FEM tool for the 2-D magnetic hysteresis modeling, Cardelli, E., Faba, A., Laudani, A., Lozito, G.M., Riganti Fulginei, F., Salvini, A., Physica B: Condensed Matter, ISSN 0921-4526, Issue , 2016.
Digital Object Identifier: 10.1016/j.physb.2015.12.006
[CrossRef]

[7] A Neural Network-Based Low-Cost Solar Irradiance Sensor, Mancilla-David, Fernando, Riganti-Fulginei, Francesco, Laudani, Antonino, Salvini, Alessandro, IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Issue 3, Volume 63, 2014.
Digital Object Identifier: 10.1109/TIM.2013.2282005
[CrossRef]

[8] Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach, Cardelli, E., Faba, A., Laudani, A., Lozito, G.M., Riganti Fulginei, F., Salvini, A., Physica B: Condensed Matter, ISSN 0921-4526, Issue , 2016.
Digital Object Identifier: 10.1016/j.physb.2015.12.005
[CrossRef]

[9] Estimation of the Earth Resistance by Artificial Neural Network Model, Asimakopoulou, Fani E., Kontargyri, Vassiliki T., Tsekouras, George J., Gonos, Ioannis F., Stathopulos, Ioannis A., IEEE Transactions on Industry Applications, ISSN 0093-9994, Issue 6, Volume 51, 2015.
Digital Object Identifier: 10.1109/TIA.2015.2427114
[CrossRef]

[10] Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels, Laudani, Antonino, Lozito, Gabriele Maria, Riganti Fulginei, Francesco, Salvini, Alessandro, International Journal of Photoenergy, ISSN 1110-662X, Issue , 2015.
Digital Object Identifier: 10.1155/2015/413654
[CrossRef]

[11] A neural approach for the numerical modeling of two-dimensional magnetic hysteresis, Cardelli, E., Faba, A., Laudani, A., Riganti Fulginei, F., Salvini, A., Journal of Applied Physics, ISSN 0021-8979, Issue 17, Volume 117, 2015.
Digital Object Identifier: 10.1063/1.4916306
[CrossRef]

[12] A Neural Network Embedded System for Real-time Estimation of Muscle Forces, Lozito, Gabriele Maria, Schmid, Maurizio, Conforto, Silvia, Fulginei, Francesco Riganti, Bibbo, Daniele, Procedia Computer Science, ISSN 1877-0509, Issue , 2015.
Digital Object Identifier: 10.1016/j.procs.2015.05.196
[CrossRef]

[13] Computer Modeling of Nickel–Iron Alloy in Power Electronics Applications, Cardelli, Ermanno, Faba, Antonio, Laudani, Antonino, Quondam Antonio, Simone, Riganti Fulginei, Francesco, Salvini, Alessandro, IEEE Transactions on Industrial Electronics, ISSN 0278-0046, Issue 3, Volume 64, 2017.
Digital Object Identifier: 10.1109/TIE.2016.2597129
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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