Unsupervised neural method for temperature forecasting

被引：37

作者：

Corchado, JM

Fyfe, C

机构：

[1] Univ Vigo, Dept Languages & Comp Syst, Ourense 32004, Spain

[2] Univ Paisley, Dept Comp & Informat Syst, Paisley PA1 2BE, Renfrew, Scotland

来源：

ARTIFICIAL INTELLIGENCE IN ENGINEERING | 1999年 / 13卷 / 04期

关键词：

time series; forecasting; modelling; adaptation; real time; oceanography;

D O I：

10.1016/S0954-1810(99)00007-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents the results of using a novel Negative Feedback Artificial Neural Network for extraction of models of the thermal structure of oceanographic water masses and to forecast time series in real time. The results obtained using this model are compared with those obtained using a Linear Regression and an ARIMA model. The article presents the Negative Feedback Artificial Neural Network, shows how it extracts the model behind the data set and discuses the Artificial Neural Network's forecasting abilities. (C) 1999 Elsevier Science Ltd. All rights reserved.

引用

页码：351 / 357

页数：7

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