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Xu, K., Song, J.D., Zhao, Y.W., Bi, Q. and Liu, Y. (2013) Forecasting Model Based on an Improved Elman Neural Network and Its Application in the Agricultural Production. IEEE International Conference on Granular Computing, Beijing, 13-15 December 2013, 202-207.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6740408

has been cited by the following article:

• TITLE: A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks

  AUTHORS: Bharath Chandra Mummadisetty, Astha Puri, Ershad Sharifahmadian, Shahram Latifi

  KEYWORDS: Data Compression, Predictive Analysis, Artificial Neural Network, Compression Ratio, Machine Learning, Climate Data Prediction

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.8 No.6, June 24, 2015

  ABSTRACT: The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.