A Comparison of Radial Basis Probabilistic Neural Network and Radial Basis Function Neural Network Performance Based on Sensitivity Analysis

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dc.contributor.authorHasanuddin, Hasanuddin
dc.date.accessioned2018-02-19T03:40:52Z
dc.date.available2018-02-19T03:40:52Z
dc.date.issued2018-02-19
dc.description.abstractThis paper presents a comparative study of the performance learning algorithm for Radial Basis Probabilistic Neural Network (RBPNN), and the Radial Basis Function Neural Network (RBFNN), are evaluated and compared for their ability to classify data based on sensitivity analysis. RBPNN generally performs similarly to RBFNN. Both of them are trained using gradient descent. In this research, sensitivity analysis is used to prune the feature data. The results show that the network still works well after pruning. The issues of network optimization and computational efficiency in use are discussed. Finally, to evaluate the performance, our experiments are demonstrated by two examples of real life data set.en_US
dc.description.sponsorshipProsiding Seminar Nasional dan Kongres IndoMS Wilayah Sumatera Bagian Tengah FMIPA Universitas Riau, 14-15 Nopember 2014en_US
dc.identifier.isbn978-979-792-552-9
dc.identifier.otherwahyu sari yeni
dc.identifier.urihttp://repository.unri.ac.id:8080/xmlui/handle/123456789/9209
dc.language.isoenen_US
dc.subjectRBPNNen_US
dc.subjectRBFNNen_US
dc.subjectpruning criteriaen_US
dc.subjectsensitivity analysisen_US
dc.subjectclassificationen_US
dc.titleA Comparison of Radial Basis Probabilistic Neural Network and Radial Basis Function Neural Network Performance Based on Sensitivity Analysisen_US
dc.typeArticleen_US

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7. SEMINAR NASIONAL MATEMATIKA DAN KONGRES THE INDONESIAN MATHEMATICAL SOCIETY WILAYAH SUMATERA BAGIAN TENGAH 2014