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Adaptive Neuro-Fuzzy Black-Box Modeling Based on Instrumental Variable Evolving Algorithm

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Abstract

In this paper, an online identification algorithm for instrumental variable-based evolving neuro-fuzzy modeling applied to dynamic systems in noisy environment is proposed. The adopted methodology is based on neuro-fuzzy inference system with Takagi–Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The application and performance analysis of the proposed algorithm is based on black-box modeling of a 2DOF Helicopter with errors in variables.

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Acknowledgements

This work was supported by FAPEMA and encouraged by Ph.D. Program in Electrical Engineering of Federal University of Maranhao (PPGEE/UFMA).

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  1. Federal Institute of Education, Science and Technology, São Luís, MA, Brazil

    Orlando Rocha & Ginalber Serra

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  1. Orlando Rocha
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  2. Ginalber Serra
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Correspondence to Ginalber Serra.

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Rocha, O., Serra, G. Adaptive Neuro-Fuzzy Black-Box Modeling Based on Instrumental Variable Evolving Algorithm. J Control Autom Electr Syst 28, 50–67 (2017). https://doi.org/10.1007/s40313-016-0285-8

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  • DOI: https://doi.org/10.1007/s40313-016-0285-8

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