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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorPrasad, Mukeshen_US
dc.contributor.authorSaxena, Amiten_US
dc.date.accessioned2015-12-02T02:59:39Z-
dc.date.available2015-12-02T02:59:39Z-
dc.date.issued2015-11-01en_US
dc.identifier.issn2168-2216en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TSMC.2015.2406855en_US
dc.identifier.urihttp://hdl.handle.net/11536/128395-
dc.description.abstractIn this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets.en_US
dc.language.isoen_USen_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectgroup methods of data handling (GMDH)en_US
dc.subjectpattern classificationen_US
dc.subjectpolynomial neural network (PNN)en_US
dc.titleAn Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TSMC.2015.2406855en_US
dc.identifier.journalIEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMSen_US
dc.citation.volume45en_US
dc.citation.issue11en_US
dc.citation.spage1389en_US
dc.citation.epage1401en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電機工程學系zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000363231900001en_US
dc.citation.woscount0en_US
Appears in Collections:Articles