基于信息粒度的广义回归神经网络模型在短期温度预测中的应用

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Studies in Informatics and Control Pub Date : 2022-09-30 DOI:10.24846/v31i3y202205
Wang Weiwei, D. Hao
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引用次数: 1

摘要

本文提出了基于信息粒度的广义回归神经网络(GRNN)模型,利用MATLAB编程进行短期温度预测。为此,本文以安徽省池州九华山景区2006 - 2015年10年间7月和8月的日平均气温数据为研究对象。将该方法与BP神经网络和高斯函数的拟合性能进行了比较。该方法不仅提高了短期预测的准确性,而且克服了数据拟合不准确的缺点。它可以略微提高短期预测的有效性和实用性,可以更有效地分析互联网上的短期数据。
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The Application of the Generalized Regression Neural Network Model Based on Information Granulation for Short-Term Temperature Prediction
: 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.
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来源期刊
Studies in Informatics and Control
Studies in Informatics and Control AUTOMATION & CONTROL SYSTEMS-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
2.70
自引率
25.00%
发文量
34
审稿时长
>12 weeks
期刊介绍: Studies in Informatics and Control journal provides important perspectives on topics relevant to Information Technology, with an emphasis on useful applications in the most important areas of IT. This journal is aimed at advanced practitioners and researchers in the field of IT and welcomes original contributions from scholars and professionals worldwide. SIC is published both in print and online by the National Institute for R&D in Informatics, ICI Bucharest. Abstracts, full text and graphics of all articles in the online version of SIC are identical to the print version of the Journal.
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