dc.contributor.authorHu, Donglin
dc.date.accessioned2016-05-24T03:07:15Z
dc.date.available2016-05-24T03:07:15Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/68014
dc.description.abstractStock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neural network prediction model with the minimum standard of empirical risk has poor generalization ability, which easy to fall into the optimal and disadvantages of local presence, we come up with RBF neural network.en_US
dc.format.extent63 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineeringen_US
dc.titleStock trading using RBF neural networksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US


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