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JCR Impact Factor: 0.699
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Issues per year: 4
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Next issue: May 2019
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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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.

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  3/2018 - 11

Implementation of High Speed Tangent Sigmoid Transfer Function Approximations for Artificial Neural Network Applications on FPGA

KOYUNCU, I. See more information about KOYUNCU, I. on SCOPUS See more information about KOYUNCU, I. on IEEExplore See more information about KOYUNCU, I. 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 (1,589 KB) | Citation | Downloads: 316 | Views: 565

Author keywords
real-time systems, field programmable gate arrays, artificial neural networks, approximation methods, transfer functions

References keywords
neural(15), fpga(9), chaotic(9), artificial(9), network(7), networks(6), implementation(6), koyuncu(5), hardware(4), computing(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 79 - 86
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03011
Web of Science Accession Number: 000442420900011
SCOPUS ID: 85052056771

Abstract
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Tangent Sigmoid (TanSig) Transfer Function (TSTF) is one of the nonlinear functions used in Artificial Neural Networks (ANNs). As TSTF includes exponential function operations, hardware-based implementation of this function is difficult. Thus, various methods have been proposed in the literature for the hardware implementation of TSTF. In this study, four different TSTF approaches on FPGA have been implemented using 32-bit IEEE 7541985 floating point number standard, and their performance analyses and FPGA chip statistics are presented. The Van der Pol system ANN application was carried out using four different FPGA-based TSTF units presented. The Multilayer feed-forward neural network structure was used in the study. The FPGA chip statistics and sensitivity analyses were carried out by applying each TSTF structure to the exemplary ANN. The maximum operating frequency of ANNs designed on FPGA using the four different TSTF units varied between 184362 MHz. The CORDIC-LUT-based ANN on FPGA was able to calculate 1 billion results in 3.284 s. According to the Van der Pol system ANN application carried out on FPGA, the CORDIC-LUT-based approach most closely reflected the reference ANN results. This study has a reference and key research for real-time artificial neural network applications used of tangent sigmoid one of the nonlinear transfer functions.


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

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[CrossRef] [Full Text] [Web of Science Times Cited 18] [SCOPUS Times Cited 19]


[2] E. Betiku, A. E. Taiwo, "Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate response surface methodology and artificial neural network," Renewable Energy, In vivo thermography-based image for early detection of breast cancer using two-tier segmentation algorithm and artificial neural network vol. 74, pp. 87-94, 2015.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 63]


[3] M. Alcin, I. Pehlivan, I. Koyuncu, "Hardware design and implementation of a novel ANN-based chaotic generator in FPGA," Optik-International Journal for Light and Electron Optics, vol. 127, pp. 5500-5505, 2016.
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[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


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[CrossRef] [Web of Science Times Cited 127] [SCOPUS Times Cited 139]


[6] M. Tuna, C. B. Fidan, "A Study on the importance of chaotic oscillators based on FPGA for true random number generating (TRNG) and chaotic systems," Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 33, pp. 469-486, 2018.
[CrossRef] [SCOPUS Times Cited 5]


[7] S. Roy, R. Banerjee, P. K. Bose, "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, vol. 119, pp. 330-340, 2014.
[CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 80]


[8] M. Alcin, I. Pehlivan, I. Koyuncu, "The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers," Balkan Journal of Electrical and Computer Engineering, vol. 3(4), pp. 252-255, 2015.
[CrossRef]


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[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 10]


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[CrossRef] [SCOPUS Times Cited 7]


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[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


[13] M. A. Cavuslu, C. Karakuzu, S. Sahin, M. Yakut, "Neural network training based on FPGA with floating point number format and it's performance," Neural Computing and Applications, vol. 20, pp. 195-202, 2011.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]


[14] A. Saad, K. Hany, A. Amin, "Three-dimensional turbulent swirling flow reconstruction using artificial neural networks," Journal of Mechanical Engineering and Automation, vol. 4, pp. 1-9, 2014.
[CrossRef]


[15] S. Brezetskyi, D. Dudkowski, T. Kapitaniak, "Rare and hidden attractors in Van der Pol-Duffing oscillators," The European Physical Journal Special Topics, vol. 224, pp. 1459-1467, 2015.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 58]


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References Weight

Web of Science® Citations for all references: 421 TCR
SCOPUS® Citations for all references: 502 TCR

Web of Science® Average Citations per reference: 20 ACR
SCOPUS® Average Citations per reference: 24 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 2019-05-22 07:24 in 135 seconds.




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


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