Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm

被引：14

作者：

Faustino, Claudio Paulo ^{[1
]}

Novaes, Camila Paiva ^{[2
]}

Pinheiro, Carlos Alberto M. ^{[1
]}

Carpinteiro, Otavio A. ^{[1
]}

机构：

[1] Univ Fed Itajuba, Res Grp Syst & Comp Engn, BR-37500903 Itajuba, MG, Brazil

[2] Natl Inst Spacial Res INPE, Div Astrophys, BR-12227010 Sao Jose Dos Campos, SP, Brazil

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2014年 / 41卷 / 02期

关键词：

Time series; Neural networks; Fuzzy logic; Clustering; Fuzzy C-Means; Holt Winters method;

D O I：

10.1007/s10462-011-9308-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Prediction models based on artificial intelligence techniques have been widely used in Time Series Forecasting in several areas. They are often fuzzy models or neural networks. This paper describes the development of neural and fuzzy models for forecasting time series of practical examples, and shows the comparisons of results between models, including the results of statistical modeling. The use of data clustering algorithms like Fuzzy C-Means is considered in fuzzy models.

引用

页码：287 / 300

页数：14

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