Wang WEIWEI1, Ding HAO2*
1 School of Applied Technology, Nanjing University of Information Science and Technology,
219 Ningliu Road, Nanjing, 210044, China
2 China Telecom Corporation Limited, Bozhou branch, 1096 Xiyi Avenue, Bozhou 236000, China
firstname.lastname@example.org (*Corresponding author)
Abstract: This paper proposes the Generalized Regression Neural Network (GRNN) model based on information granularity and using MATLAB programming for short-term temperature prediction. In this respect, it focuses on the daily average temperature data for the months of July and August for a period of ten years (from 2006 to 2015) for the Jiuhua Mountain scenic spot of Chizhou, in the Anhui Province. The performance of the proposed method is compared with that of the Back Propagation (BP) neural network and with that of the Gauss function for data fitting. This method not only improves the accuracy of short-term prediction, but it also overcomes the disadvantage of inaccurate data fitting. It can slightly improve the effectiveness and practicability of short-term prediction, and it can more effectively analyze short-term data on the Internet.
Keywords: Information Granulation, GRNN neural network, BP neural network, Fourier function, Gauss function.
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CITE THIS PAPER AS:
Wang WEIWEI, Ding HAO, The Application of the Generalized Regression Neural Network Model Based on Information Granulation for Short-Term Temperature Prediction, Studies in Informatics and Control, ISSN 1220-1766, vol. 31(3), pp. 53-62, 2022. https://doi.org/10.24846/v31i3y202205