9th Symposium on Neural Network Applications in Electrical Engineering, Belgrade, Serbien

Abbrevation

NEUREL

Link

http://neurel.etf.bg.ac.yu/

City

Belgrade

Country

Serbia

Deadline Paper

2008-05-15

Start Date

2008-09-25

End Date

2008-09-27

Abstract

<SPAN lang=EN–US style=″FONT–SIZE: 9pt; COLOR: black; FONT–FAMILY: ′Arial′,′sans–serif′; mso–fareast–font–family: 宋体; mso–bidi–font–family: ′Times New Roman′; mso–fareast–theme–font: minor–fareast; mso–fareast–language: EN–US; mso–ansi–language: EN–US; mso–bidi–language: AR–SA″>The NEUREL Symposia, started in 1990, are biannual scientific meetings covering all aspects of the artificial neural networks and fuzzy systems, from their theoretical foundations to applications. Keywords: <TABLE class=MsoTableGrid style=″BORDER–RIGHT: medium none; BORDER–TOP: medium none; BORDER–LEFT: medium none; WIDTH: 412.75pt; BORDER–BOTTOM: medium none; BORDER–COLLAPSE: collapse; mso–yfti–tbllook: 480; mso–padding–alt: 0cm 5.4pt 0cm 5.4pt; mso–border–insideh: none; mso–border–insidev: none″ cellSpacing=0 cellPadding=0 width=550 border=0> <TBODY> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 0; mso–yfti–firstrow: yes″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; BACKGROUND: #cccccc; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt″ vAlign=top width=183> 1.Theory and models <?xml:namespace prefix = o ns = ″urn:schemas–microsoft–com:office:office″ /></TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; BACKGROUND: #cccccc; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt″ vAlign=top width=183> 2. Realizations</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; BACKGROUND: #cccccc; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt″ vAlign=top width=183> 3. Applications</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 1″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.1 Learning methods</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.1 Feedforward networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.1 Signal and image processing</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 2″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.2 Cognitive modeling</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.2 Recurrent neural networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.2 Robotics and control systems</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 3″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.3 Mathematics of neural systems</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.3 Self–organizing networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.3 Intelligent multimedia</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 4″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.4 Approximation theory</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.4 Cellular neural networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.4 Data mining</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 5″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.5 Computational neuroscience</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.5 Associative memories </TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.5 Communications</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 6″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.6 Bayesian models </TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.6 Hybrid architectures</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.6 Feature extraction and retrieval</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 7″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.7 Fuzzy systems</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.7 Fuzzy neural networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.7 Classification and clustering</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 8″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.8 Bio–inspired systems</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.8 Opto–electronic neural networks</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.8 Bioinformatics</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 9″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.9. Complex systems and chaos</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.9 Cognitive systems</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.9 Time series forecasting</TD></TR> <TR style=″HEIGHT: 11.35pt; mso–yfti–irow: 10; mso–yfti–lastrow: yes″> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.55pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 1.10 Fractals and multifractals</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 2.10 Brain–computer interfaces</TD> <TD style=″BORDER–RIGHT: #ece9d8; PADDING–RIGHT: 5.4pt; BORDER–TOP: #ece9d8; PADDING–LEFT: 5.4pt; PADDING–BOTTOM: 0cm; BORDER–LEFT: #ece9d8; WIDTH: 137.6pt; PADDING–TOP: 0cm; BORDER–BOTTOM: #ece9d8; HEIGHT: 11.35pt; BACKGROUND–COLOR: transparent″ vAlign=top width=183> 3.10 Power systems</TD></TR></TBODY></TABLE>