Evolving Systems

, Volume 7, Issue 2, pp 107–116 | Cite as

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

  • Yevgeniy V. Bodyanskiy
  • Oleksii K. Tyshchenko
  • Daria S. Kopaliani
Original Paper

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.

Keywords

Evolving system Learning method Extended neo-fuzzy neuron Online generalization Cascade network  Data stream 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Control Systems Research LaboratoryKharkiv National University of Radio ElectronicsKharkivUkraine

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