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Third International Conference on Information Technology and Applications (ICITA'05) Volume 1
Quantum NN vs. NN in Signal Recognition
Sydney, Australia
July 04-July 07
ISBN: 0-7695-2316-1
ASCII Text
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Xin-Yi Tsai, Yu-Ju Chen, Huang-Chu Huang, Shang-Jen Chuang, Rey-Chue Hwang, "Quantum NN vs. NN in Signal Recognition," Information Technology and Applications, International Conference on, vol. 1, pp. 308-312, Third International Conference on Information Technology and Applications (ICITA'05) Volume 1, 2005.
 
 
BibTex
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@article{ 10.1109/ICITA.2005.228,
author = {Xin-Yi Tsai and Yu-Ju Chen and Huang-Chu Huang and Shang-Jen Chuang and Rey-Chue Hwang},
title = {Quantum NN vs. NN in Signal Recognition},
journal ={Information Technology and Applications, International Conference on},
volume = {1},
year = {2005},
isbn = {0-7695-2316-1},
pages = {308-312},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICITA.2005.228},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Information Technology and Applications, International Conference on
TI - Quantum NN vs. NN in Signal Recognition
SN - 0-7695-2316-1
SP308
EP312
A1 - Xin-Yi Tsai,
A1 - Yu-Ju Chen,
A1 - Huang-Chu Huang,
A1 - Shang-Jen Chuang,
A1 - Rey-Chue Hwang,
PY - 2005
KW - signal recognition
KW - quantum neural network
KW - fuzziness
VL - 1
JA - Information Technology and Applications, International Conference on
ER -
 
Xin-Yi Tsai, I-Shou University
Yu-Ju Chen, Cheng-Shiu University
Huang-Chu Huang, National Kaohsiung Marine University
Shang-Jen Chuang, National Kaohsiung Marine University
Rey-Chue Hwang, I-Shou University
In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two- dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.
Index Terms:
signal recognition, quantum neural network, fuzziness
Citation:
Xin-Yi Tsai, Yu-Ju Chen, Huang-Chu Huang, Shang-Jen Chuang, Rey-Chue Hwang, "Quantum NN vs. NN in Signal Recognition," icita, vol. 1, pp.308-312, Third International Conference on Information Technology and Applications (ICITA'05) Volume 1, 2005
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