Name: Ensembles of Type 2 Fuzzy Neural Models and Their Optimization With Bio-Inspired Algorithms for Time Series Prediction (Springerbriefs in Applied Sciences and Technology) (en Inglés)
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Author: Jesus Soto
ISBN: 9783319712635

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portada Ensembles of Type 2 Fuzzy Neural Models and Their Optimization With Bio-Inspired Algorithms for Time Series Prediction (Springerbriefs in Applied Sciences and Technology) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Jesus Soto

Año

2017

Idioma

Inglés

N° páginas

108

Encuadernación

Tapa Blanda

ISBN13

9783319712635

N° edición

Categorías

Inteligencia artificial

Ensembles of Type 2 Fuzzy Neural Models and Their Optimization With Bio-Inspired Algorithms for Time Series Prediction (Springerbriefs in Applied Sciences and Technology) (en Inglés)

Jesus Soto (Autor) · Springer · Tapa Blanda

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Reseña del libro "Ensembles of Type 2 Fuzzy Neural Models and Their Optimization With Bio-Inspired Algorithms for Time Series Prediction (Springerbriefs in Applied Sciences and Technology) (en Inglés)"

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.