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  • GC-CSP 2012
  • 28.Cnf-310
BEN Congresses with Published Papers
GC-BEN 2011, Amman
9
Papers Published at GC-CSP 2012
All 10 Papers
IDAuthors and TitlePages
28.Cnf-303 Mr. Muhammad Yameen Sandhu
Mr. Sharjeel Afridi
Mr. Yahya Junejo
Designing of high Q dielectric resonator filter by 3-D finite element method (FEM)
1-6
28.Cnf-309 Dr. Shebel Alsabbah
Evaluation of Self-Tuned Fuzzy-PID Controller over Titration Process
7-12
28.Cnf-310 Prof. Engin Avci
Radar Target Recognition Based on Wavelet - ELM
13-18
28.Cnf-315 Mr. João Coucelo
Prof. Rui Marinheiro
Prof. Joao Silva
Prof. Jose Moura
WLAN-UMTS Integration to Optimize MBMS Provision
19-23
28.Cnf-316 Mr. Alberto Pineda
Mr. Luis Zabala
Mr. Armando Ferro
Network Architecture to Automatically Test Traffic Monitoring Systems
24-29
28.Cnf-317 Mr. Eduard Bonada
Dolors Sala
Characterizing the convergence time of RSTP
30-35
28.Cnf-318 Mr. João Rendeiro
Prof. Rui Marinheiro
Prof. Jose Moura
Prof. Joao Silva
An Adaptive Management Proposal for Optimizing the Performance of A Virtualized Computing Environment
36-40
28.Cnf-319

GS Citations

Prof. Jose Moura
Prof. Joao Silva
Prof. Rui Marinheiro
A brokerage system for enhancing wireless access
41-46
28.Cnf-330 Mr. Amirreza Naderipour
Prof. Abdullah Asuhaimi Mohd Zin
The Peruse of Designing a Hybrid Filter to Omission Harmonics and Reactive Power Improvement in Distributed Generation systems
47-52
28.Cnf-342 Dr. Kamal R. Al-Rawi
Reduction of the State Space for the Travelling Salesman Problem (TSP) Through Elimination of Unpaved Edges
53-57
28.Cnf-310 Paper View Page
Title Radar Target Recognition Based on Wavelet - ELM
Authors Prof. Engin Avci, Firat Üniversitesi, Elazig, Turkey
Abstract In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X–band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms [1-25]. To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) [1-25] which randomly choose hidden nodes and analytically determines the output weights of SLFNs. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.
Track SPA: Signal Processing and Applications
Conference 2nd Mosharaka International Conference on Communications and Signal Processing (MIC-CSP 2012)
Congress 2012 Global Congress on Communications and Signal Processing (GC-CSP 2012), 6-8 April 2012, Barcelona, Spain
Pages 13-18
Topics Wavelet Processing
Neural Networks
ISSN 2227-331X
DOI
BibTeX @inproceedings{310CSP2012,
title={Radar Target Recognition Based on Wavelet - ELM},
author={Engin Avci},
booktitle={2012 Global Congress on Communications and Signal Processing (GC-CSP 2012)},
year={2012},
pages={13-18},
doi={}},
organization={Mosharaka for Research and Studies} }
Paper Views 52 Paper Views Rank 136/524
Paper Downloads 20 Paper Downloads Rank 168/524
GC-CSP 2012 Visits: 11203||MIC-CSP 2012 Visits: 9098||SPA Track Visits: 1964