A multi-factor prediction algorithm in big data computing environments Online publication date: Thu, 01-Sep-2016
by Hao Tang; Dawei Sun
International Journal of Computing Science and Mathematics (IJCSM), Vol. 7, No. 4, 2016
Abstract: In big data environments, many problems become more huge and complex. This makes it harder for us to deal with the data and forecast the result. Traditional prediction algorithm is more suitable for less factor problem, and how to solve multi-factor problem is one of the major challenges. In this paper, a more accurate and effective algorithm was proposed based on the good performance of weight optimisation of PSO and generalisation ability of BPNN. Further, the convergence and feasibility of the combinational algorithm were analysed with actual data. The simulation revealed that with the increase of influencing factors, the BP neutral network optimised by PSO possessed a more rapid convergence rate of MSE and higher accuracy of network output values.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com