Abstract:

Abstract Context. The task of automation of diagnostic models synthesys in diagnostics and pattern recognition problems is solved. The object of the research are the methods of the neuro-fuzzy diagnostic models synthesys. The subject of the research are the methods of additional training of neuro-fuzzy networks. Objective. The research objective is to create a method for additional training of neuro-fuzzy diagnostic models. Method. The method of additional training of diagnostic neuro-fuzzy models is proposed. It allows to adapt existing models to the change in the functioning environment by modifying them taking into account the information obtained as a result of new observations. This method assumes the stages of extraction and grouping the correcting instances, diagnosing them with the help of the existing model leads to incorrect results, as well as the construction of a correcting block that summarizes the data of the correcting instances and its implementation into an already existing model. Using the proposed method of learning the diagnostic neural-fuzzy models allows not to perform the resource-intensive process of re-constructing the diagnostic model on the basis of a complete set of data, to use the already existing model as the computing unit of the new model. Models synthesized using the proposed method are highly interpretive, since each block generalizes information about its data set and uses neuro-fuzzy models as a basis. Results. The software which implements the proposed method of additional training of neuro-fuzzy networks and allows to reconfigure the existing diagnostic models based on new information about the researched objects or processes based on the new data has been developed. Conclusions. The conducted experiments have confirmed operability of the proposed method of additional training of neurofuzzy networks and allow to recommend it for processing of data sets for diagnosis and pattern recognition in practice. The prospects for further researches may include the development of the new methods for the additional training of deep learning neural networks for the big data processing. References Suzuki K. Artificial Neural Networks: Architectures and Applications. New York, InTech, 2013, 264 p. DOI:

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