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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
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Next issue: Nov 2018
Avg review time: 81 days


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

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


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2012 - 6

Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

TIMCHENKO, L. See more information about TIMCHENKO, L. on SCOPUS See more information about TIMCHENKO, L. on IEEExplore See more information about TIMCHENKO, L. on Web of Science, KOKRIATSKAIA, N. See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about KOKRIATSKAIA, N. on Web of Science, MELNIKOV, V. See more information about  MELNIKOV, V. on SCOPUS See more information about  MELNIKOV, V. on SCOPUS See more information about MELNIKOV, V. on Web of Science, MAKARENKO, R. See more information about  MAKARENKO, R. on SCOPUS See more information about  MAKARENKO, R. on SCOPUS See more information about MAKARENKO, R. on Web of Science, PETROVSKYI, N. See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on Web of Science
 
Click to see author's profile in 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 (718 KB) | Citation | Downloads: 386 | Views: 1,917

Author keywords
parallel-hierarchical network, training, population coding, preparation, face recognition

References keywords
timchenko(6), hierarchical(6), processing(5), recognition(4), parallel(4), neural(4), networks(4), network(4), learning(4), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 39 - 46
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04006
Web of Science Accession Number: 000312128400006
SCOPUS ID: 84872764925

Abstract
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Full text preview
Propositions necessary for development of parallel-hierarchical (PH) network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute) similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.


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

[1] W. S. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133, 1943.
[CrossRef] [SCOPUS Times Cited 5773]


[2] L. I. Timchenko, V. V. Melnikov, N.I. Kokryatskaya, Yu. F. Kutaev, I.D. Ivasyuk. A method of organization of a parallel-hierarchical network for image recognition. Journal Cybernetics and system analysis. , Vol.47 (1), pp. 140-151, 2011.
[CrossRef] [SCOPUS Times Cited 7]


[3] L. I. Timchenko, V. V. Melnikov, N.I. Kodryatskaya, Parallel-hierarchical network learning methods and their application to pattern recognition, Cybernetics and Systems Analysis, 47(6), 2011.
[CrossRef] [SCOPUS Times Cited 2]


[4] M. Hirahara, N. Oka, T. Kindo. A cascade associative memory model with a hierarchical memory structure. Journal Neural Networks, Vol.13, Issue 1, pp. 41-50, 2000.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]


[5] J. Sacramento, A. Wichert. Tree-like hierarchical associative memory structures. Journal Neural Networks, pp. 143-147, 2010. [PubMed]

[6] L. I. Timchenko. A multistage parallel-hierarchic network as a model of a neurolike computation scheme. Journal Cybernetics and system analysis. - Vol.36(2), pp. 251-267, 2000.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


[7] L. I. Timchenko, Y. F. Kutaev, S.V. Chepornyuk, M.A. Grudin, A.A. Gertsiy.A brain-like approach to multistage hierarchical image processing. Springer-Verlag Processing. - in Proc. Image Analysis and Processing, Florence, Italy, pp. 246 - 253, 1997.

[8] D. E Hinton. How do neural networks train? In the world of science, 11, 1992.

[9] B. Widrow, and M. A. Lehr. 30 years of adaptive neural networks: Perceptron, madaline and backpropagation. Proceedings of the Institute of Electrical and Electronics Engineers, Vol. 78, p. 1415-1442, 1990.
[CrossRef] [Web of Science Times Cited 1036] [SCOPUS Times Cited 1281]


[10] T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer, 2001.

[11] S. Gadat, L. Younes. A stochastic algorithm for feature selection in pattern recognition. Research Journal of Machine Learning Research (8), pp. 509-547, 2007.

[12] L. I. Timchenko, N. I. Kokryatskaya, A.A. Poplavskyy, A.A Poplavska, I.D. Ivasyuk. Method of reference tunnel formation for improvement of forecast results of laser beams spot images behavior. 18th International Conference IWSSIP-2011, pp. 1-3., 2011b.

[13] Manchester base of human faces. [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] L. I. Timchenko, Y. F. Kutaev, V. P. Kozhemyako, et al. Method for Training of a Parallel-Hierarchical Network, Based on Population Coding for Processing of Extended Laser Paths Images. Proceedings of SPIE, Vol. 4790, pp. 465-479, 2002.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 18]


[15] Tom Mitchel. Machine Learning . McGraw Hill, 432p, 1997.

[16] V. P. Kozhemyako, E. I. Ponuraya, V. Belokonniy. Logic-temporal functions processing for object recognition. Selected papers from the International Conference on Optoelectronic Information Technologies. Bellingham, Wash., USA, SPIE,+ Vol.4425, pp. 35-40, 2001.



References Weight

Web of Science® Citations for all references: 1,069 TCR
SCOPUS® Citations for all references: 7,102 TCR

Web of Science® Average Citations per reference: 63 ACR
SCOPUS® Average Citations per reference: 418 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 2018-11-15 00:34 in 54 seconds.




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


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