Application of Artificial Neural Networks for Temperature Forecasting
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.
[1] I. Maqsood, I., Khan, M.R., Abraham, A., Intelligent weather monitoring systems using connectionist models. International Journal of
Neural, Parallel and Scientific Computations, No. 10, 2000, pp.157-178.
[2] I. Maqsood, M.R. Khan, A. Abraham, Neuro-computing based Canadian
weather analysis, The 2nd International Workshop on Intelligent
Systems Design and Applications, Computational Intelligence and
Applications. Dynamic Publishers, Atlanta, Georgia, 2002, pp. 39-44.
[3] L. Fausett, Fundamental of Neural Networks, New York , Prentice Hall. A well-written book, with very detailed worked examples to explain
how the algorithm function, 1994.
[4] J.M. Zurada, Introduction to Artificial Neural Systems, West Publishing
Company, Saint Paul, Minnesota, 1992.
[5] M.T. Hagan. H.B. Demuth, M.H. Beale. Neural Network Design. PWS
Publishing Company, Boston, Massachusetts, 1996.
[6] S. Haykin. Neural Networks, A Comprehensive Foundation, New York,
Macmillan Publishing. A comprehensive book, with an engineering
perspective. Requires a good mathematical background , and contains a
great deal of background theory, 1994.
[7] C. Bishop, Neural Networks for Pattern Recognition, University press.
Extremely well-Written, up-to-date. Require a good mathematical
background , but rewards careful reading, putting neural networks firmly
into a statistical context, 1995.
[8] D. Patterson. Artificial neural networks. Singapore, Prentice Hall. Good
wide-ranging coverage of topics, although less detailed than some other
books, 1996.
[9] S. Haykin, Neural networksÔÇöa comprehensive foundation. Prentice-
Hall, New Jersey, 1999.
[10] A. Moller. Scaled Conjugate Gradient Algorithm for Fast Supervised
Learning, Neural Networks, 6 (4), 1993, pp.525-533.
[11] I. Maqsood, M.R. Khan, A. Abraham, Canadian weather analysis using
connectionist learning paradigms. The Seventh Online World Conference
on Soft Computing in Industrial Application, On the Internet. Springer,
Germany, 2000.
[12] Y. Linde, A. Buzo, R. Gray, An algorithm for vector quantizer design.
IEEE Transactions on Communications, No. 28, 1980, pp. 84-95.
[13] P.P. Vander Smagt, Minimization methods for training feedforward
neural networks. Neural Networks, 1, 1994, pp.1-11.
[1] I. Maqsood, I., Khan, M.R., Abraham, A., Intelligent weather monitoring systems using connectionist models. International Journal of
Neural, Parallel and Scientific Computations, No. 10, 2000, pp.157-178.
[2] I. Maqsood, M.R. Khan, A. Abraham, Neuro-computing based Canadian
weather analysis, The 2nd International Workshop on Intelligent
Systems Design and Applications, Computational Intelligence and
Applications. Dynamic Publishers, Atlanta, Georgia, 2002, pp. 39-44.
[3] L. Fausett, Fundamental of Neural Networks, New York , Prentice Hall. A well-written book, with very detailed worked examples to explain
how the algorithm function, 1994.
[4] J.M. Zurada, Introduction to Artificial Neural Systems, West Publishing
Company, Saint Paul, Minnesota, 1992.
[5] M.T. Hagan. H.B. Demuth, M.H. Beale. Neural Network Design. PWS
Publishing Company, Boston, Massachusetts, 1996.
[6] S. Haykin. Neural Networks, A Comprehensive Foundation, New York,
Macmillan Publishing. A comprehensive book, with an engineering
perspective. Requires a good mathematical background , and contains a
great deal of background theory, 1994.
[7] C. Bishop, Neural Networks for Pattern Recognition, University press.
Extremely well-Written, up-to-date. Require a good mathematical
background , but rewards careful reading, putting neural networks firmly
into a statistical context, 1995.
[8] D. Patterson. Artificial neural networks. Singapore, Prentice Hall. Good
wide-ranging coverage of topics, although less detailed than some other
books, 1996.
[9] S. Haykin, Neural networksÔÇöa comprehensive foundation. Prentice-
Hall, New Jersey, 1999.
[10] A. Moller. Scaled Conjugate Gradient Algorithm for Fast Supervised
Learning, Neural Networks, 6 (4), 1993, pp.525-533.
[11] I. Maqsood, M.R. Khan, A. Abraham, Canadian weather analysis using
connectionist learning paradigms. The Seventh Online World Conference
on Soft Computing in Industrial Application, On the Internet. Springer,
Germany, 2000.
[12] Y. Linde, A. Buzo, R. Gray, An algorithm for vector quantizer design.
IEEE Transactions on Communications, No. 28, 1980, pp. 84-95.
[13] P.P. Vander Smagt, Minimization methods for training feedforward
neural networks. Neural Networks, 1, 1994, pp.1-11.
@article{"International Journal of Electrical, Electronic and Communication Sciences:57565", author = "Mohsen Hayati and Zahra Mohebi", title = "Application of Artificial Neural Networks for Temperature Forecasting ", 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. ", keywords = "Artificial neural networks, Forecasting, Weather,Multi-layer perceptron.", volume = "1", number = "4", pages = "653-5", }
