• Ye. V. Bodyanskiy Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • A. O. Deineko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • Ya. V. Kutsenko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine



hybrid system, Data Mining, Data Stream Mining, neuro-fuzzy system, membership function, fuzzy clustering.


An on-line neuro-fuzzy system for solving data stream fuzzy clustering task and its self-learning procedures based on T. Kohonen’s rule are proposed in the paper. The architecture of proposed system consists of seven information processing layers and represents the hybrid of the Wang-Mendel system and clustering selforganizing network. During a learning procedure in on-line mode, the proposed system tunes both its parameters and its architecture. For tuning of membership
functions parameters of neuro-fuzzy system the method based on competitive learning is proposed. The hybrid neuro-fuzzy system tunes its synaptic weights, centers and width parameters of membership functions. Software that implements the proposed hybrid neuro-fuzzy system’s architecture has been developed. A number of experiments has been held in order to research the proposed system’s properties. Experimental results have proved the fact that the proposed system could be used to solve a sequential stream clustering task. The proposed system provides computational simplicity. A distinguishing feature of the proposed system is that this system combine supervised learning and self-learning procedures.


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How to Cite

Bodyanskiy, Y. V., Deineko, A. O., & Kutsenko, Y. V. (2016). SEQUENTIAL FUZZY CLUSTERING BASED ON NEURO-FUZZY APPROACH. Radio Electronics, Computer Science, Control, (3).



Neuroinformatics and intelligent systems

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