Support vector machine assisted genetic programming for MQAM classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{ZhechenZhu:2011:ISSCS,
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author = "Zhechen Zhu and Muhammad Waqar Aslam and
Asoke Kumar Nandi",
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title = "Support vector machine assisted genetic programming
for MQAM classification",
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booktitle = "10th International Symposium on Signals, Circuits and
Systems (ISSCS 2011)",
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year = "2011",
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month = "30 " # jun # "-1 " # jul,
-
address = "lasi, Romania",
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size = "6 pages",
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abstract = "Automatic modulation classification is used to
identify automatically the modulation type of an
incoming signal with limited or no prior knowledge to
it. Various classifier systems have been developed to
solve this problem. However, for certain types of
modulations such as 16 QAM and 64 QAM, the
classification performance under noisy condition still
needs to be improved. In this paper, we propose a new
AMC scheme by combining genetic programing (GP) with
support vector machine (SVM) for the classification of
16 QAM and 64 QAM signals. The benchmark result shows
that SVM assisted GP can produce better accuracy than
some other existing methods.",
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keywords = "genetic algorithms, genetic programming, AMC scheme,
GP, MQAM classification, QAM signals, SVM, automatic
modulation classification, classification performance,
classifier systems, modulation type, noisy condition,
support vector machine assisted genetic programming,
quadrature amplitude modulation, signal classification,
support vector machines",
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DOI = "doi:10.1109/ISSCS.2011.5978654",
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notes = "Also known as \cite{5978654}",
- }
Genetic Programming entries for
Zhechen Zhu
Muhammad Waqar Aslam
Asoke K Nandi
Citations