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Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

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Abstract

This paper proposes an architecture and learning algorithms for a cascade neo-fuzzy system based on pools of extended neo-fuzzy neurons. The proposed system is different from existing cascade systems in its capability to operate in an online mode, which allows it to work with non-stationary and stochastic non-linear chaotic signals that come in the form of data streams. A new pool optimization procedure is introduced. Compared to conventional analogues, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

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Authors and Affiliations

  1. Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 14 Nauka Ave, Kharkiv, 61166, Ukraine

    Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko & Daria S. Kopaliani

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  1. Yevgeniy V. Bodyanskiy
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  2. Oleksii K. Tyshchenko
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  3. Daria S. Kopaliani
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Correspondence to Oleksii K. Tyshchenko.

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Bodyanskiy, Y.V., Tyshchenko, O.K. & Kopaliani, D.S. Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks. Evolving Systems 7, 107–116 (2016). https://doi.org/10.1007/s12530-016-9149-5

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  • DOI: https://doi.org/10.1007/s12530-016-9149-5

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