The paper describes a modification of the neo-fuzzy neuron called as «extended neo-fuzzy neuron» (ENFN) that characterized by improved approximating properties. The adaptive learning algorithm for ENFN is proposed, that has both following and smoothing properties and allows to solve problems of prediction, filtering and smoothing of non-stationary disturbed stochastic and chaotic signals. A distinctive feature of ENFN is its implementation computational simplicity compared with artificial neural networks and neuro-fuzzy systems. These properties of the proposed neo-fuzzy neuron make it very effective in suppressing noise in image filtering.
Науковий журнал «Радіоелектроніка, інформатика, управління»
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Bodyanskiy, Ye. V.; Kharkiv National University of Radio Electronics, Ukraine Kulishova, N. E.; Kharkiv National University of Radio Electronics, Ukraine |
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The paper describes a modification of the neo-fuzzy neuron called as «extended neo-fuzzy neuron» (ENFN) that characterized by improved approximating properties. The adaptive learning algorithm for ENFN is proposed, that has both following and smoothing properties and allows to solve problems of prediction, filtering and smoothing of non-stationary disturbed stochastic and chaotic signals. A distinctive feature of ENFN is its implementation computational simplicity compared with artificial neural networks and neuro-fuzzy systems. These properties of the proposed neo-fuzzy neuron make it very effective in suppressing noise in image filtering. |
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Zaporizhzhya National Technical University 2014-08-20 00:00:00 |
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application/pdf http://ric.zntu.edu.ua/article/view/27281 |
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Radio Electronics, Computer Science, Control; No 1 (2014): Radio Electronics, Computer Science, Control |
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Copyright (c) 2014 Ye. V. Bodyanskiy, N. E. Kulishova |
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