Forecasting Number of Students Applicant for Courses by Artificial Neural Networks

Volume 12, Issue 4, pp 263-270 http://dx.doi.org/10.22436/jmcs.012.04.02

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 2490 Downloads
• 3455 Views

Authors

Jafar Pouramini - Department of Computer Engineering and Information Technology, Payame Noor University(PNU), Iran.

Abstract

Forecasting the number of students who are going to take a special course in next semester in Computer Engineering field at Payam Noor University is the subject. To do this, many neural network structures have been tested with MATLAB software by existing data and were compared to real data, networks like feedforward backpropagation 3 and 4-layared, RBF network, etc. To achieve a network with optimum structure, various parameters and criteria like MAE, MSE and MSEREG, have been examined. At last, a 3-layered feedback neural network in the form of 20-n-1 was chosen for this problem. Comparing experiential results with real data, it is shown that the obtained model can effectively forecast enrolments of students. So it can be used for forecasting tasks especially when a forecast with high accuracy is needed.

Share and Cite

Keywords

• Artificial Neural Network
• RBF network
• Elman network
• Hopfield network
• Forecasting

MSC

•  62M45
•  92B20

References

• [1] Saeed Ayat, Zabihollah Ahmad Pour, Comparison between artificial neural network learning algorithms for prediction of student average considering effective factors in Learning and educational progress, Journal of mathematics and computer science , 8 (2014), 215 - 225.
  • Google Scholar


• [2] Ali Ghezelbash, Predicting changes in stock index and gold prices to neural network approach, The Journal of mathematics and computer science , 4(2) (2012), 227-236.
  • Google Scholar


• [3] Mahnaz Bagheri , The bankruptcy prediction in Tehran share holding using neural and it’s comparision with logistic regression, The Journal of mathematics and computer science , 5(3) (2012), 219-228.
  • Google Scholar


• [4] Mehdi Sotoudeh, Elahe Farshad, Application of neural network for forecasting gas price in America, The Journal of mathematics and computer science , 4(2) (2012), 216-226.
  • Google Scholar


• [5] Mehdi Sotoudeh, Elahe Farshad , Application of neural network for forecasting gas price in America, Journal of mathematics and computer science , 4(2) (2012), 216- 226.
  • Google Scholar


• [6] k. Abhishek, M. P. Singh, S. Ghosh, A. Anand, Weather forecasting model using artificial neural network, procedia technology, 4 (2012), 311-318.
  • View Article
  • Google Scholar


• [7] M. Qi, G. P. Zhang, An Investigation of model selection criteria for neural network time series forecasting, european journal of operational research, 132 (2001), 666-680.
  • View Article
  • Google Scholar
  • MATH


• [8] Ufuk Yolcu, Erol Egrioglu, Cagdas H. Aladag , A new linear & nonlinear artificial neural network model for time series forecasting, Decision Support Systems, 54 (2013), 1340-1347.
  • View Article
  • Google Scholar


• [9] Juan Peralta Donate, Paulo Cortez, Germán Gutiérrez Sánchez, Araceli Sanchis de Miguel, Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble, Neurocomputing, 109 (2013), 27-32.
  • View Article
  • Google Scholar


• [10] I. Kaastra, M. Boyd, Designing a neural network for forecasting financial and economic time series, neurocomputing, 10 (1996), 215-236.
  • View Article
  • Google Scholar


• [11] E. Azoff, Neural network time series forecasting of financial markets, John Wiley & Sons, (1994)
  • Google Scholar