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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: Aug 2018
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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 »


    
 

  2/2011 - 15

A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks

AHN, C. K. See more information about AHN, C. K. on SCOPUS See more information about AHN, C. K. on IEEExplore See more information about AHN, C. K. 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 (605 KB) | Citation | Downloads: 1,097 | Views: 2,719

Author keywords
passive filtering, multilayer Hopfield neural networks, linear matrix inequality (LMI), external disturbance

References keywords
neural(11), networks(11), state(5), control(5), systems(4), delayed(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-05-30
Volume 11, Issue 2, Year 2011, On page(s): 95 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.02015
Web of Science Accession Number: 000293840500015
SCOPUS ID: 79958851524

Abstract
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Full text preview
This paper investigates the passivity based filtering problem for multilayer Hopfield neural networks with external disturbance. A new passivity based filter design method for multilayer Hopfield neural networks is developed to ensure that the filtering error system is exponentially stable and passive from the external disturbance vector to the output error vector. The unknown gain matrix is obtained by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.


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

[1] J. J. Hopfield. Neurons with grade response have collective computational properties like those of two-state neurons. Proc. Nat. Acad. Sci., 81:3088-3092, 1984.
[CrossRef] [Web of Science Times Cited 3706] [SCOPUS Times Cited 4065]


[2] M. M. Gupta, L. Jin, and N. Homma. Static and Dynamic Neural Networks. Wiley-Interscience, 2003.
[CrossRef] [SCOPUS Times Cited 47]


[3] Z. Wang, D. W. C. Ho, and X. Liu. State estimation for delayed neural networks. IEEE Trans. Neural Networks, 16:279-284, 2005.
[CrossRef] [PubMed] [Web of Science Times Cited 260] [SCOPUS Times Cited 276]


[4] Y. He, Q. G. Wang, M. Wu, and C. Lin. Delay-dependent state estimation for delayed neural networks. IEEE Trans. Neural Networks, 17:1077-1081, 2006.
[CrossRef] [PubMed] [Web of Science Times Cited 146] [SCOPUS Times Cited 165]


[5] L. Jin, P. N. Nikiforuk, and M. M. Gupta. Adaptive control of discrete time nonlinear systems using recurrent neural networks. IET Proceedings Control Theory and Application, 141:169-176, 1994.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 61]


[6] Y. Liu, Z. Wang, and X. Liu. Design of exponential state estimators for neural networks with mixed time delays. Phys. Lett. A, 364:401-412, 2007.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 129]


[7] H. Huang and G. Feng. Delay-dependent and generalized filtering for delayed neural networks. IEEE Trans. Circ. Syst. I, 56:846-857, 2009.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 82]


[8] Z. Wang, Y. Liu, and X. Liu. State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Networks, 22:41-48, 2009.
[CrossRef] [PubMed] [Web of Science Times Cited 204] [SCOPUS Times Cited 210]


[9] J. C. Willems. Dissipative dynamical systems, part I: General theory. Arch. Rational Mech. Anal., 45:321-351, 1972.
[CrossRef] [SCOPUS Times Cited 1794]


[10] C. I. Byrnes, A. Isidori, and J. C. Willem. Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear system. IEEE Trans. Automat. Contr., 36:1228-1240, 1991.
[CrossRef] [Web of Science Times Cited 740] [SCOPUS Times Cited 908]


[11] C. K. Ahn. Linear matrix inequality approach to passive filtering for delayed neural networks. Journal of Systems and Control Engineering, 224:1040-1047, 2010.

[12] S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishinan. Linear matrix inequalities in systems and control theory. SIAM, Philadelphia, PA, 1994.

[13] P. Gahinet, A. Nemirovski, A. J. Laub, and M. Chilali. LMI Control Toolbox. The Mathworks Inc., 1995.



References Weight

Web of Science® Citations for all references: 5,288 TCR
SCOPUS® Citations for all references: 7,737 TCR

Web of Science® Average Citations per reference: 378 ACR
SCOPUS® Average Citations per reference: 553 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 2018-07-21 00:25 in 73 seconds.




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


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