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.
The paper proposes the ways to apply swarm intelligence strategies to parallelize neuroevolution methods for synthesizing artificial neural networks. The proposed approaches will solve a number of problems that usually arise during designing high-performance computing related to the synthesis of neural networks. The object of research is the process of developing a parallel approach for the neuroevolution synthesis of artificial neural networks, namely, the use of swarm intelligence strategies to solve a number of problems in designing a method that would use the resources of a parallel computer system. One of the most problematic areas is the highly adaptive nature and significant operating time of neuroevolution methods. One way to solve these problems is to use parallel computer systems and distributed computing. However, a number of questions arise when designing a parallel neuroevolution method. During research a number of tasks were solved, which included the analysis and study of neuroevolution methods for synthesizing artificial neural networks and problems of their parallelization. Attention is also paid to swarm intelligence methods, which have gained popularity recently and show good results. The new method developed during the work was based on strategies for organizing work with swarm particles. Thus, sub-populations distributed between threads and individuals were analyzed as individual particles that interact with each other and depend on the local environment. Classical genetic operators were modified by criterion mechanisms to improve adaptability. During the experiments, the developed method was compared with classical methods. During the work, special attention was paid not only to the characteristics of the resulting neuromodels, but also to the load on the processor during Operation. The developed method showed acceptable results for all comparisons. The new approach has significantly improved the quality level of the parallel neuroevolution synthesis method, allowing to evenly use the capabilities of computing nodes in a parallel system.
Context. The problem of structural modification of pre-synthesized models based on artificial neural networks to ensure the property of interpretation when working with big data is considered. The object of the study is the process of structural modification of artificial neural networks using adaptive mechanisms. Objective of the work is to develop a method for structural modification of neural networks to increase their speed and reduce resource consumption when processing big data. Method. A method of structural adjustment of neural networks based on adaptive mechanisms borrowed from neuroevolutionary synthesis methods is proposed. At the beginning, the method uses a system of indicators to evaluate the existing structure of an artificial neural network. The assessment is based on the structural features of neuromodels. Then the obtained indicator estimates are compared with the criteria values for choosing the type of structural changes. Variants of mutational changes from the group of methods of neuroevolutionary modification of the topology and weights of the neural network are used as variants of structural change. The method allows to reduce the resource intensity during the operation of neuromodels, by accelerating the processing of big data, which expands the field of practical application of artificial neural networks. Results. The developed method is implemented and investigated by the example of using a recurrent artificial network of the long short-term memory type when solving the classification problem. The use of the developed method allowed speed up of the neuromodel with a test sample by 25.05%, depending on the computing resources used. Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in the structural adjustment of pre-synthesized neuromodels for further solving problems of diagnosis, forecasting, evaluation and pattern recognition using big data. The prospects for further research may consist in a more fine-tuning of the indicator system to determine the connections encoding noisy data in order to further improve the accuracy of models based on neural networks.
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