Academic Journal
Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty
| العنوان: | Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty |
|---|---|
| المؤلفون: | Alexey S. Katasev |
| المصدر: | Компьютерные исследования и моделирование, Vol 11, Iss 3, Pp 477-492 (2019) |
| بيانات النشر: | Institute of Computer Science, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Applied mathematics. Quantitative methods LCC:Mathematics |
| مصطلحات موضوعية: | neuro-fuzzy model, fuzzy neural network, fuzzy production rule, knowledge base formation, object state evaluation, Applied mathematics. Quantitative methods, T57-57.97, Mathematics, QA1-939 |
| الوصف: | This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | Russian |
| تدمد: | 2076-7633 2077-6853 |
| Relation: | http://crm.ics.org.ru/uploads/crmissues/crm_2019_3/2019_03_09.pdf; https://doaj.org/toc/2076-7633; https://doaj.org/toc/2077-6853 |
| DOI: | 10.20537/2076-7633-2019-11-3-477-492 |
| URL الوصول: | https://doaj.org/article/90c83dbeb3504421acf3ab52f0972b4c |
| رقم الانضمام: | edsdoj.90c83dbeb3504421acf3ab52f0972b4c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20767633 20776853 |
|---|---|
| DOI: | 10.20537/2076-7633-2019-11-3-477-492 |