OPTIMIZATION OF ENSEMBLE NEURAL NETWORKS WITH TYPE-2 FUZZY INTEGRATION OF RESPONSES FOR THE DOW JONES TIME SERIES PREDICTION

被引：8

作者：

Melin, Patricia ^{[1
]}

Pulido, Martha ^{[1
]}

机构：

[1] Tijuana Inst Technol, Fracc Tomas Aquino 22379, Tijuana, Mexico

来源：

INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2014年 / 20卷 / 03期

关键词：

Ensemble Neural Networks; Genetic Algorithms; Optimization; Time Series Prediction; MODEL; SYSTEMS; LOGIC;

D O I：

10.1080/10798587.2014.893047

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes an optimization method based on genetic algorithms for designing ensemble neural networks with fuzzy response aggregation to forecast complex time series. The time series that was considered in this paper, to compare the hybrid approach with traditional methods, is the Dow Jones data, and the results are presented for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy response integration. Simulation results show that the ensemble approach produces 99% prediction accuracy for the Dow Jones time series and that using type-2 fuzzy logic improves the performance of the predictor.

引用

页码：403 / 418

页数：16

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