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Big Data Analytics and Fuzzy Technology: Extracting Information from Social Data Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Ensemble Neural Network with Type-2 Fuzzy Weights Using Response Integration for Time Series Prediction

Authors : Fernando Gaxiola, Patricia Melin, Fevrier Valdez, Juan R. Castro

Published in: Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Publisher: Springer International Publishing

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Abstract

In this paper an ensemble of three neural networks with type-2 fuzzy weights is proposed. One neural network uses type-2 fuzzy inference systems with Gaussian membership functions for obtain the fuzzy weights; the second neural network uses type-2 fuzzy inference systems with triangular membership functions; and the third neural network uses type-2 fuzzy inference systems with triangular membership functions with uncertainty in the standard deviation. Average integration and type-2 fuzzy integrator are used for the results of the ensemble neural network. The proposed approach is applied to a case of time series prediction, specifically in the Mackey-Glass time series.

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previous chapter Optimization of Modular Neural Network Architectures with an Improved Particle Swarm Optimization Algorithm
next chapter Resource Selection with Soft Set Attribute Reduction Based on Improved Genetic Algorithm
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Metadata
Title
Ensemble Neural Network with Type-2 Fuzzy Weights Using Response Integration for Time Series Prediction
Authors
Fernando Gaxiola
Patricia Melin
Fevrier Valdez
Juan R. Castro
Copyright Year
2018
Book
Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-75407-9

Electronic ISBN: 978-3-319-75408-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75408-6_15

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