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Ensembles of type 2 fuzzy neural mod...

Castillo, Oscar.

Ensembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ensembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series predictionby Jesus Soto, Patricia Melin, Oscar Castillo.
Author:	Soto, Jesus.
other author:	Melin, Patricia.
Published:	Cham :Springer International Publishing :2018.
Description:	viii, 97 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-319-71264-2
ISBN:	9783319712642$q(electronic bk.)

Ensembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series prediction
Soto, Jesus.

Ensembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series prediction[electronic resource] /by Jesus Soto, Patricia Melin, Oscar Castillo. - Cham :Springer International Publishing :2018. - viii, 97 p. :ill. (some col.), digital ;24 cm. - Springerbriefs in applied sciences and technology,2191-530X. - Springerbriefs in applied sciences and technology..

This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.

ISBN: 9783319712642$q(electronic bk.)

Standard No.: 10.1007/978-3-319-71264-2doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Ensembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series prediction
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490 1 $a Springerbriefs in applied sciences and technology, $x 2191-530X
520 $a This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.
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