Optimal construction of the pattern matrix for probabilistic neural networks in technical diagnostics based on expert estimations

Скорочена інформація про документ
dc.contributor.authorRomanuke, Vadim
dc.date.accessioned2023-04-18T15:27:32Z
dc.date.available2023-04-18T15:27:32Z
dc.date.issued2021
dc.description.abstractIn the field of technical diagnostics, many tasks are solved by using automated classification. For this, such classifiers like probabilistic neural networks fit best owing to their simplicity. To obtain a probabilistic neural network pattern matrix for technical diagnostics, expert estimations or measurements are commonly involved. The pattern matrix can be deduced straightforwardly by just averaging over those estimations. However, averages are not always the best way to process expert estimations. The goal is to suggest a method of optimally deducing the pattern matrix for technical diagnostics based on expert estimations. The main criterion of the optimality is maximization of the performance, in which the subcriterion of maximization of the operation speed is included. First of all, the maximal width of the pattern matrix is determined. The width does not exceed the number of experts. Then, for every state of an object, the expert estimations are clustered. The clustering can be done by using the k-means method or similar. The centroids of these clusters successively form the pattern matrix. The optimal number of clusters determines the probabilistic neural network optimality by its performance maximization. In general, most results of the error rate percentage of probabilistic neural networks appear to be near-exponentially decreasing as the number of clustered expert estimations is increased. Therefore, if the optimal number of clusters defines a too “wide” pattern matrix whose operation speed is intolerably slow, the performance maximization implies a tradeoff between the error rate percentage minimum and maximally tolerable slowness in the probabilistic neural network operation speed. The optimal number of clusters is found at an asymptotically minimal error rate percentage, or at an acceptable error rate percentage which corresponds to maximally tolerable slowness in operation speed. The optimality is practically referred to the simultaneous acceptability of error rate and operation speed.uk
dc.format.pagerangePp. 19-25uk
dc.identifier.citationRomanuke, V. Optimal construction of the pattern matrix for probabilistic neural networks in technical diagnostics based on expert estimations / Romanuke Vadim // Information, Computing and Intelligent systems. – 2021. – No. 2. – Pp. 19–25. – Bibliogr.: 13 ref.uk
dc.identifier.doihttps://doi.org/10.20535/2708-4930.2.2021.244186
dc.identifier.orcid0000-0003-3543-3087uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/54695
dc.language.isoenuk
dc.publisherNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"uk
dc.publisher.placeKyivuk
dc.relation.ispartofInformation, Computing and Intelligent systems, No. 2uk
dc.subjecttechnical diagnosticsuk
dc.subjectprobabilistic neural networkuk
dc.subjectpattern matrixuk
dc.subjectexpert estimationsuk
dc.subjectclusteringuk
dc.subjectperformance maximizationuk
dc.subject.udc004.383.3, 004.056, 004.8uk
dc.titleOptimal construction of the pattern matrix for probabilistic neural networks in technical diagnostics based on expert estimationsuk
dc.typeArticleuk

Файли

Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
244186-568443-1-10-20211216.pdf
Розмір:
846.28 KB
Формат:
Adobe Portable Document Format
Опис:
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Ескіз недоступний
Назва:
license.txt
Розмір:
9.1 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити

Зібрання

Information, Computing and Intelligent systems, No. 2