SEQUENTIAL FUZZY CLUSTERING BASED ON NEURO-FUZZY APPROACH
Науковий журнал «Радіоелектроніка, інформатика, управління»
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SEQUENTIAL FUZZY CLUSTERING BASED ON NEURO-FUZZY APPROACH |
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Bodyanskiy, Ye. V.; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine Deineko, A. O.; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine Kutsenko, Ya. V.; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine |
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hybrid system, Data Mining, Data Stream Mining, neuro-fuzzy system, membership function, fuzzy clustering. |
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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<br />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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Zaporizhzhya National Technical University 2016-10-27 12:28:19 |
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application/pdf http://ric.zntu.edu.ua/article/view/81234 |
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Radio Electronics, Computer Science, Control; No 3 (2016): Radio Electronics, Computer Science, Control |
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uk |
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Copyright (c) 2016 Ye. V. Bodyanskiy, A. O. Deineko, Ya. V. Kutsenko |
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