Context. The task of automation of diagnostic models synthesys in diagnostics and pattern recognition problems is solved. The<br />object of the research are the methods of the neuro-fuzzy diagnostic models synthesys. The subject of the research are the methods of<br />additional training of neuro-fuzzy networks.<br />Objective. The research objective is to create a method for additional training of neuro-fuzzy diagnostic models.<br />Method. The method of additional training of diagnostic neuro-fuzzy models is proposed. It allows to adapt existing models to<br />the change in the functioning environment by modifying them taking into account the information obtained as a result of new observations.<br />This method assumes the stages of extraction and grouping the correcting instances, diagnosing them with the help of the<br />existing model leads to incorrect results, as well as the construction of a correcting block that summarizes the data of the correcting<br />instances and its implementation into an already existing model. Using the proposed method of learning the diagnostic neural-fuzzy<br />models allows not to perform the resource-intensive process of re-constructing the diagnostic model on the basis of a complete set of<br />data, to use the already existing model as the computing unit of the new model. Models synthesized using the proposed method are<br />highly interpretive, since each block generalizes information about its data set and uses neuro-fuzzy models as a basis.<br />Results. The software which implements the proposed method of additional training of neuro-fuzzy networks and allows to reconfigure<br />the existing diagnostic models based on new information about the researched objects or processes based on the new data<br />has been developed.<br />Conclusions. The conducted experiments have confirmed operability of the proposed method of additional training of neurofuzzy<br />networks and allow to recommend it for processing of data sets for diagnosis and pattern recognition in practice. The prospects<br />for further researches may include the development of the new methods for the additional training of deep learning neural networks<br />for the big data processing.
Науковий журнал «Радіоелектроніка, інформатика, управління»
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Oliinyk, A.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. Subbotin, S.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. Leoshchenko, S.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. Ilyashenko, M.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. Myronova, N.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. Mastinovsky, Y.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine. |
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Context. The task of automation of diagnostic models synthesys in diagnostics and pattern recognition problems is solved. The<br />object of the research are the methods of the neuro-fuzzy diagnostic models synthesys. The subject of the research are the methods of<br />additional training of neuro-fuzzy networks.<br />Objective. The research objective is to create a method for additional training of neuro-fuzzy diagnostic models.<br />Method. The method of additional training of diagnostic neuro-fuzzy models is proposed. It allows to adapt existing models to<br />the change in the functioning environment by modifying them taking into account the information obtained as a result of new observations.<br />This method assumes the stages of extraction and grouping the correcting instances, diagnosing them with the help of the<br />existing model leads to incorrect results, as well as the construction of a correcting block that summarizes the data of the correcting<br />instances and its implementation into an already existing model. Using the proposed method of learning the diagnostic neural-fuzzy<br />models allows not to perform the resource-intensive process of re-constructing the diagnostic model on the basis of a complete set of<br />data, to use the already existing model as the computing unit of the new model. Models synthesized using the proposed method are<br />highly interpretive, since each block generalizes information about its data set and uses neuro-fuzzy models as a basis.<br />Results. The software which implements the proposed method of additional training of neuro-fuzzy networks and allows to reconfigure<br />the existing diagnostic models based on new information about the researched objects or processes based on the new data<br />has been developed.<br />Conclusions. The conducted experiments have confirmed operability of the proposed method of additional training of neurofuzzy<br />networks and allow to recommend it for processing of data sets for diagnosis and pattern recognition in practice. The prospects<br />for further researches may include the development of the new methods for the additional training of deep learning neural networks<br />for the big data processing. |
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Zaporizhzhya National Technical University 2018-12-07 16:07:43 |
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application/pdf http://ric.zntu.edu.ua/article/view/149787 |
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Radio Electronics, Computer Science, Control; No 3 (2018): Radio Electronics, Computer Science, Control |
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Copyright (c) 2018 A. Oliinyk, S. Subbotin, S. Leoshchenko, M. Ilyashenko, N. Myronova, Y. Mastinovsky |
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