Seasonal forecasting yield of wheat in India by feed-forward neural networks

References

• Anderson, J. A., “Introduction to Neural Networks”, Cambridge, MA: MIT Press, 1995.
   Google Scholar
• Bakirtzis, A. G., Petridis, V., Kiartzis, S. J., Alexiadis, M. C., and Maissis, A. H., “A Neural Network Short Term Load Forecasting Model for The Greek Power System”, IEEE Transactions on Power Systems, Vol.11, Issue 2, pp 858-863, 1996. doi: 10.1109/59.496166
   Web of Science ®Google Scholar
• Box, G. E. P., Jenkins, G. M. And Reinsel, G. C., “Time Series Analysis Forecasting and Control”, 3rd ed., Englewood Cliffs, N. J. Prentice Hall, 1994.
   Google Scholar
• De Gooijer, J.G., Hyndman, J.R., “25 Years of Time Series Forecasting”, International Journal of Forecasting, Vol. 22, pp 443–473, 2006. doi: 10.1016/j.ijforecast.2006.01.001
   Web of Science ®Google Scholar
• [5] Fuller, W. A., “Introduction to Statistical Time Series”, Wiley, New York, 1976.
   Google Scholar
• Haykin, S. S., “Neural Networks: A Comprehensive Foundation”, Upper Saddle River, N. J., Prentice Hall, 1999.
   Google Scholar
• Hippert, H. S., Pedreira, C. E., & Souza, R. C., “Neural networks for short-term load forecasting: A review and evaluation”, IEEE Transactions on Power Systems, Vol. 16, Issue 1, pp 44-55, 2001. doi: 10.1109/59.910780
   Web of Science ®Google Scholar
• Ljung, G. M. and Box, G. E. P., “On A Measure of Lack of Fit in Time Series Models”, Biometrika, Vol. 65, pp 297–303, 1978. doi: 10.1093/biomet/65.2.297
   Web of Science ®Google Scholar
• Montgomery, D. C., L. A. Jhonson, and J. S. Gardiner, “Forecasting and Time Series Analysis”, 2nd ed., New York: McGraw-Hill, 1990.
   Google Scholar
• Morteza Tak., Yahya Ajabshirchi and Asghar Mahmoudi. “Prediction of output energy for wheat production using artificial neural networks in Esfahan province of Iran”, Journal of Agricultural Technology, Vol. 8, Issue 4, pp 1229-1242, 2012.
   Google Scholar
• Puja Mondal. Wheat Cultivation in India: Conditions, and Distribution. Your Article Library.Link:http://www.yourarticlelibrary.com/cultivation/wheat-cultivation-in-India-conditions-anddistribution/20924, 2015.
   Google Scholar
• Rajiv Gupta, Manish A. Kewalramani and Amit Goel, Prediction of Concrete Strength Using Neural-Expert System, Journal of Materials in Civil Engineering, 18(3), 2006. doi: 10.1061/(ASCE)0899-1561(2006)18:3(462)
   Web of Science ®Google Scholar
• R. Ramakrishna, Naveen Kumar Boiroju and M. Krishna Reddy, Forecasting Daily Electricity Load Using Neural Networks, International Journal of Mathematical Archive, 2(8), pp 1341-1351, 2011.
   Google Scholar
• R. Ramakrishna, Naveen Kumar Boiroju and M. Krishna Reddy, Neural Networks Forecasting Model for Monthly Electricity Load in Andhra Pradesh, International Journal of Engineering Research and Applications, 2(1), pp 1108-1115, 2012.
   Google Scholar
• Ramakrishna, R., Naveen Kumar, B., Forecasting Yield Per Hectare of Rice in Andhra Pradesh, International Journal of Mathematics and Computer Applications Research, Vol. 3, Issue 1, pp 9-14, 2013.
   Google Scholar
• Ramakrishna, R., “Neural Networks Forecasting Yield per Hectare of Jowar :A Case Study “,Ceser Publications, International Journal of Ecological Economics and Statistics, Volume 37, Issue No.1, pp 55-62, 2016.
   Google Scholar
• Sachin Kumar and Naarendra kumar, “Fuzzy Time Series based Method for Wheat production Forecasting” International Journal of Computer Applications (0975-8887) Vol. 44, Issue 12, pp 5-10, 2012 doi: 10.5120/6313-8651
   Google Scholar
• Schwarz, G., “Estimating the Dimension of a Model”, Annals of Statistics, Vol. 6, No. 2, pp 461-464, 1978. doi: 10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Sharma. E.R and Rudrakshi. E.R.,”Enhancing Back Propagation Neural N/w Algorithm for crop prediction” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 6, June - 2014, pp. 1368-1372, 2014.
   Google Scholar
• Tang, Z., Almeida, C. D. and Fishwick, P. A., “Time Series Forecasting using Neural Networks Vs. Box-Jenkins Methodology”, Simulation, Vol. 57, No. 5, pp 303-310, 1991. doi: 10.1177/003754979105700508
   Google Scholar
• Türker Karaman, Prediction of Multiphase Flow Properties From Nuclear Magnetic Resonance Imaging, PhD thesis, Middle East Technical University, 2009.
   Google Scholar
• Vermaak, J. & Botha, E. C., “Recurrent Neural Networks for Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol.13, Issue 1, pp126-132, 1998. doi: 10.1109/59.651623
   Web of Science ®Google Scholar
• Wang Fei, Mi Zengqiang, Su Shi and Zhang Chengcheng, A practical model for single-step power prediction of grid-connected PV plant using artificial neural network, IEEE PES Innovative Smart Grid Technologies, 13-16 Nov, 2011.
   Google Scholar
• Willmott, C. J., “On the Validation of Models”, Physical Geography, Vol. 2, pp184-194, 1981. doi: 10.1080/02723646.1981.10642213
   Google Scholar
• Willmott, C.J., “Some Comments on the Evaluation of Model Performance”, Bulletin of the American Meteorological Society, Vol. 63, pp 1309-1313, 1982. doi: 10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2
   Web of Science ®Google Scholar
• Yegnanarayana, B., “Artificial Neural Networks“, Prentice Hall
   Google Scholar
• Zhang, G., Patuwo, B.E. and Hu, M.Y., 1998, “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, Vol.14, pp 35-62, 1999. doi: 10.1016/S0169-2070(97)00044-7
   Web of Science ®Google Scholar