Аdditional Training of Neuro-Fuzzy Diagnostic Models

Oliinyk, Аndrii; Subbotin, Sergey; Leoshchenko, Serhii; Ilyashenko, M.; Myronova, Natalia; Mastinovsky, Y.

doi:10.15588/1607-3274-2018-3-12

Cited by 2 publications

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“…Нейроеволюційні методи передбачають використання специфічних генетичних операторів [11,22,23]. В ряді робіт пропонувалося використовувати адаптивні механізми мутації.…”

Section: матеріали та методиunclassified

“…В ряді робіт пропонувалося використовувати адаптивні механізми мутації. Загалом визначимо основні види мутацій, що можуть використовуватися при синтезі ШНМ [22,23]:…”

Section: матеріали та методиunclassified

“…Під час тестування будемо звертати увагу на час роботи мережі та розподіл часу за ітераціями. Додатково будемо оцінювати точність роботи [22,23]. Для цього введемо відносну помилку, яка в цьому випадку буде обчислюватися, як відношення кількості невірно діагностованих об'єктів до загального розміру вибірки (кількості її екземплярів):…”

Section: виходячи з цього коефіцієнт логічної прозорості шнмunclassified

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The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

Leoshchenko¹,

Oliinyk²,

Субботин³

et al. 2021

RIC

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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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Section: матеріали та методиunclassified

Section: виходячи з цього коефіцієнт логічної прозорості шнмunclassified

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The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

Leoshchenko¹,

Oliinyk²,

Субботин³

et al. 2021

RIC

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Implementation of the Indicator System in Modeling of Complex Technical Systems

Leoshchenko

Субботин

Oliinyk

et al. 2021

RIC

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Context. The problem of determining the optimal topology of a neuromodel, which is characterized by a high level of logical transparency in modeling complex technical systems, is considered. The object of research is the process of applying an indicator system to simplify and select the topology of neuromodels. Objective of the work is to develop and use a system of indicators to determine the level of complexity of the modeling problem and gradually select the optimal logically transparent topology of the neuromodel. Method. A method is proposed for selecting an optimal, logically transparent neural network topology for modeling complex technical systems using a system of corresponding indicators. At the beginning, the method determines the overall level of complexity of the modeling task and, using the obtained estimate, determines the method for further optimization of the neuromodel. Then, using Task data and input data characteristics, the method allows to obtain the most optimal structure of the neural model for further modeling of the system. The method reduces trainingvtime and increases the level of logical transparency of neuromodels, which significantly expands the practical use of such models, without using neuroevolution methods, which may not be justified by resource-intensive tasks. Results. The developed method is implemented and investigated in solving the problem of modeling the dynamics of pitting processes of steel alloys. Using the developed method made it possible to reduce the training time of the model by 22%, depending on the computing resources used. The method also increased the level of logical transparency of the model by reducing the number of computing nodes by 50%, which also indicates faster and more efficient use of resources. Conclusions. The conducted experiments confirmed the operability of the proposed mathematical support and allow us to recommend it for use in practice in the design of topologies of neuromodels for further solving modeling, diagnosis and evaluation problems. Prospects for further research may consist in the development of methods for structural optimization of previously synthesized models and the development of new methods for feature selection.

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Аdditional Training of Neuro-Fuzzy Diagnostic Models

Cited by 2 publications

References 0 publications

The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

Implementation of the Indicator System in Modeling of Complex Technical Systems

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