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Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction

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Abstract

Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey–Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo & Patricia Melin

  2. UABC University, Tijuana, BC, Mexico

    Juan R. Castro & Antonio Rodriguez-Diaz

Authors
  1. Oscar Castillo
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  2. Juan R. Castro
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  3. Patricia Melin
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  4. Antonio Rodriguez-Diaz
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Corresponding author

Correspondence to Oscar Castillo.

Additional information

Communicated by J. M. Garibaldi.

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Castillo, O., Castro, J.R., Melin, P. et al. Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction. Soft Comput 18, 1213–1224 (2014). https://doi.org/10.1007/s00500-013-1139-y

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  • DOI: https://doi.org/10.1007/s00500-013-1139-y

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