COMPARING THE PERFORMANCE OF PREDICTIVE MODELS CONSTRUCTED USING THE TECHNIQUES OF FEED-FORWARD AND GENERALIZED REGRESSION NEURAL NETWORKS

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dc.contributor.authorAjiboye, A.R.
dc.contributor.authorAbdullah-Arshah, R.
dc.contributor.authorHonqwu, Q.
dc.contributor.authorAbdul-Hadi, J.
dc.date.accessioned2018-11-30T14:31:36Z
dc.date.available2018-11-30T14:31:36Z
dc.date.issued2016-02
dc.descriptionArticleen_US
dc.description.abstractConstruction of predictive model is primarily aimed at using the known attributes to determine the present or the future unknown attributes for efficient planning and decision making. The accuracy of predictive model is therefore, paramount to achieving network outputs that are well correlated with the known or target output. In this paper, two predictive models are constructed using the techniques of feed-forward and generalized regression neural networks. Experiments are conducted with a Matlab software and the performance of the two models is evaluated for accuracy. Their simulated outputs are compared to determine their response to untrained data. Findings from this study show that, the generalized regression neural network consistently shows a more accurate result. The Mean Absolute Error computed for the two models also reveals that, feed-forward neural network records higher error value.en_US
dc.identifier.citationInternational Journal of Software Engineering & Computer Sciencesen_US
dc.identifier.issn2289-8522
dc.identifier.urihttp://hdl.handle.net/123456789/1300
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Pahangen_US
dc.relation.ispartofseries;Volume 2
dc.subjectPredictive modelen_US
dc.subjectFeed-forward networken_US
dc.subjectGeneralized Regressionen_US
dc.subjectPredictionen_US
dc.subjectBack- Propagationen_US
dc.subjectNeural networken_US
dc.titleCOMPARING THE PERFORMANCE OF PREDICTIVE MODELS CONSTRUCTED USING THE TECHNIQUES OF FEED-FORWARD AND GENERALIZED REGRESSION NEURAL NETWORKSen_US
dc.typeArticleen_US

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