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Genetic Optimization of Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Prediction

  • Jesus Soto
  • Patricia Melin
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

This chapter describes the genetic optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Mackey-Glass time series. The considered a chaotic system is he Mackey-Glass time series that is generated from the differential equations, so this benchmarks time series is used for the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of memberships function parameters of each interval type-2 fuzzy integrators. In the experiments we optimized Gaussians, Generalized Bell and Triangular membership functions for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.

Keywords

ANFIS Time series Interval type-2 fuzzy Ensemble learning Genetic algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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