{"title":"Application of Artificial Neural Networks for Temperature Forecasting ","authors":"Mohsen Hayati, Zahra Mohebi","volume":4,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":662,"pagesEnd":667,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/8486","abstract":"In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems. ","references":"[1] I. Maqsood, I., Khan, M.R., Abraham, A., Intelligent weather monitoring systems using connectionist models. International Journal of\r\nNeural, Parallel and Scientific Computations, No. 10, 2000, pp.157-178.\r\n[2] I. Maqsood, M.R. Khan, A. Abraham, Neuro-computing based Canadian\r\nweather analysis, The 2nd International Workshop on Intelligent\r\nSystems Design and Applications, Computational Intelligence and\r\nApplications. Dynamic Publishers, Atlanta, Georgia, 2002, pp. 39-44.\r\n[3] L. Fausett, Fundamental of Neural Networks, New York , Prentice Hall. A well-written book, with very detailed worked examples to explain\r\nhow the algorithm function, 1994.\r\n[4] J.M. Zurada, Introduction to Artificial Neural Systems, West Publishing\r\nCompany, Saint Paul, Minnesota, 1992.\r\n[5] M.T. Hagan. H.B. Demuth, M.H. Beale. Neural Network Design. PWS\r\nPublishing Company, Boston, Massachusetts, 1996.\r\n[6] S. Haykin. Neural Networks, A Comprehensive Foundation, New York,\r\nMacmillan Publishing. A comprehensive book, with an engineering\r\nperspective. Requires a good mathematical background , and contains a\r\ngreat deal of background theory, 1994.\r\n[7] C. Bishop, Neural Networks for Pattern Recognition, University press.\r\nExtremely well-Written, up-to-date. Require a good mathematical\r\nbackground , but rewards careful reading, putting neural networks firmly\r\ninto a statistical context, 1995.\r\n[8] D. Patterson. Artificial neural networks. Singapore, Prentice Hall. Good\r\nwide-ranging coverage of topics, although less detailed than some other\r\nbooks, 1996.\r\n[9] S. Haykin, Neural networks\u00d4\u00c7\u00f6a comprehensive foundation. Prentice-\r\nHall, New Jersey, 1999.\r\n[10] A. Moller. Scaled Conjugate Gradient Algorithm for Fast Supervised\r\nLearning, Neural Networks, 6 (4), 1993, pp.525-533.\r\n[11] I. Maqsood, M.R. Khan, A. Abraham, Canadian weather analysis using\r\nconnectionist learning paradigms. The Seventh Online World Conference\r\non Soft Computing in Industrial Application, On the Internet. Springer,\r\nGermany, 2000.\r\n[12] Y. Linde, A. Buzo, R. Gray, An algorithm for vector quantizer design.\r\nIEEE Transactions on Communications, No. 28, 1980, pp. 84-95.\r\n[13] P.P. Vander Smagt, Minimization methods for training feedforward\r\nneural networks. Neural Networks, 1, 1994, pp.1-11.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 4, 2007"}