基于不同气象序列的温度预测

2012 Third Global Congress on Intelligent Systems Pub Date : 2012-11-06 DOI:10.1109/GCIS.2012.103

J. L. Vásquez, C. Travieso, T. S. Perez, J. B. Alonso, J. Briceño

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引用次数: 1

摘要

本文设计并实现了一个基于不同系列气象数据的温度预报系统。利用人工神经网络多层感知器，以5个样本作为气象数据的窗口大小进行预测。此外，对浮点算法进行了评估，其均方误差为0.35，与实际温度相差0.28摄氏度。为了展示我们的最佳方案，我们将采用不同的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Temperature Prediction Based on Different Meteorological Series

In this work, a temperature predictor has been designed and implemented based on different series of meteorological data. The prediction is built by an artificial neural network multilayer perceptron, using 5 samples as window size of meteorological data. Besides, the floating point algorithm was evaluated, reaching a mean square error of 0.35, meaning a variation of 0.28 Celsius degrees versus the real temperature. Different approaches will be applied in order to show our best proposal.

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2012 Third Global Congress on Intelligent Systems

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