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  • © 2018

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

  • 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

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)

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eBook USD 54.99
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  • ISBN: 978-3-319-71264-2
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Table of contents (5 chapters)

  1. Front Matter

    Pages i-viii
  2. Introduction

    • Jesus Soto, Patricia Melin, Oscar Castillo
    Pages 1-3
  3. State of the Art

    • Jesus Soto, Patricia Melin, Oscar Castillo
    Pages 5-15
  4. Problem Statement and Development

    • Jesus Soto, Patricia Melin, Oscar Castillo
    Pages 17-34
  5. Simulation Studies

    • Jesus Soto, Patricia Melin, Oscar Castillo
    Pages 35-86
  6. Conclusion

    • Jesus Soto, Patricia Melin, Oscar Castillo
    Pages 87-88
  7. Back Matter

    Pages 89-97

About this book

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.

 

Keywords

  • Computational Intelligence
  • Intelligent Systems
  • Fuzzy Systems
  • Hybrid Systems
  • Type 2 Fuzzy Neural Models

Authors and Affiliations

  • Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Jesus Soto, Patricia Melin, Oscar Castillo

Bibliographic Information

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

  • Authors: Jesus Soto, Patricia Melin, Oscar Castillo

  • Series Title: SpringerBriefs in Applied Sciences and Technology

  • DOI: https://doi.org/10.1007/978-3-319-71264-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Author(s) 2018

  • Softcover ISBN: 978-3-319-71263-5

  • eBook ISBN: 978-3-319-71264-2

  • Series ISSN: 2191-530X

  • Series E-ISSN: 2191-5318

  • Edition Number: 1

  • Number of Pages: VIII, 97

  • Number of Illustrations: 28 b/w illustrations, 73 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

Buying options

eBook USD 54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-71264-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 69.99
Price excludes VAT (USA)