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An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm

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Abstract

In this paper, we propose an evolving interval type-2 neuro-fuzzy inference system and its meta-cognitive learning algorithm. The antecedent of each rule is an interval type-2 fuzzy set and the consequent realizes the Takagi–Sugeno–Kang inference mechanism. It employs a data driven mechanism for type-reduction and uncertainty is modeled using both the antecedent and consequent parameters. The learning starts with zero rules and evolves the network parameters and structure as a new sample arrives, using a meta-cognitive learning algorithm. The meta-cognitive learning algorithm regulates learning by employing adequate learning strategies. It uses gradient-descent to adapt the network parameters. The proposed algorithm is referred to as meta-cognitive interval type-2 neuro-fuzzy inference system-gradient descent (McIT2FIS-GD). The performance of McIT2FIS-GD is evaluated using a set of benchmark and a real-world wind speed prediction problem and compared with existing type-1 and type-2 systems. The results highlight that performance of McIT2FIS-GD is superior than other compared algorithms.

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Acknowledgments

This research was supported through Energy research institute through the grant sponsored by Economic development board of Singapore (EIRP Grant NRF2013EWT-EIRP003-032). We would like to thank the anonymous reviewers for their valuable comments which has improved the quality of paper.

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    A. K. Das, Nguyen Anh & Sundaram Suresh

  2. Energy Research Institute, Nanyang Technological University, Singapore, Singapore

    N. Srikanth

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  1. A. K. Das
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  2. Nguyen Anh
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  3. Sundaram Suresh
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  4. N. Srikanth
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Correspondence to A. K. Das.

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Das, A.K., Anh, N., Suresh, S. et al. An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm. Evolving Systems 7, 95–105 (2016). https://doi.org/10.1007/s12530-016-9148-6

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  • DOI: https://doi.org/10.1007/s12530-016-9148-6

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