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Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction

  • Kartonierter Einband
  • 97 Seiten
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Beschreibung

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.



Includes a brief introduction, where the intelligent techniques that are used, the main contribution, motivations, application, and a general description of the proposed methods are presented

Focuses on the fields of hybrid systems, fuzzy systems, bio-inspired algorithms and time series

Describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks (IT2FNN) models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction

Includes supplementary material: sn.pub/extras



Inhalt
Introduction.- State of Art.- Problem Statement and Development.- Simulation Studies.- Conclusion.

Produktinformationen

Titel: Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction
Autor:
EAN: 9783319712635
ISBN: 978-3-319-71263-5
Format: Kartonierter Einband
Herausgeber: Springer, Berlin
Genre: Technik
Anzahl Seiten: 97
Gewicht: 190g
Größe: H6mm x B235mm x T155mm
Jahr: 2017
Untertitel: Englisch
Auflage: 1st ed. 2018

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