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Time Series Prediction Using Ensembles of ANFIS Models with Particle Swarm Optimization of the Fuzzy Integrators

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Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

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Abstract

This paper describes the Particle Swarm Optimization of the Fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction time series. A chaotic system is considered in this work, which is the Mackey-Glass time series, that is generated from a model is in the form of differential equations. This benchmark time series is used to test of performance of the proposed optimization of the fuzzy integrators in ensemble architecture. We used interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Particle Swarm Optimization (PSO) was used for the optimization of membership function parameters of each fuzzy integrator. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameters for each of the fuzzy integrators. Simulation results show the effectiveness of the proposed approach. Therefore, a comparison was made against another recent work to validate the performance of the proposed model.

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Authors and Affiliations

  1. Division of Graduates Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Jesus Soto, Patricia Melin & Oscar Castillo

Authors
  1. Jesus Soto
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  2. Patricia Melin
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  3. Oscar Castillo
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Corresponding author

Correspondence to Jesus Soto .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

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Soto, J., Melin, P., Castillo, O. (2015). Time Series Prediction Using Ensembles of ANFIS Models with Particle Swarm Optimization of the Fuzzy Integrators. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_39

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  • DOI: https://doi.org/10.1007/978-3-319-27060-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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