Отрывок: (1) Second layer performs a fuzzification operation. Activation function of the neurons of this layer is a membership function of the corresponding term of the linguistic variable. In this case, the Gaussian function is selected as the activation function: g = Mxij (mij,σij)2 = − (ui(2)−mij)2σij2 , a = eg, j = 1, hı�����. (2) Number of neurons of the third layer is equal to the number of ...
Название : Development of the Structure of the Knowledge Base for Neuro-Fuzzy Diagnostic System
Авторы/Редакторы : A.V. Nikonov
A.M. Vulfin
M.M. Gayanova
M.U. Sapozhnikova
Ключевые слова : data mining
neural network
fuzzy logic
Дата публикации : 2018
Издательство : Новая техника
Библиографическое описание : A.V. Nikonov. Development of the Structure of the Knowledge Base for Neuro-Fuzzy Diagnostic System / A.V. Nikonov, A.M. Vulfin, M.M. Gayanova, M.U. Sapozhnikova // Сборник трудов IV международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2018) - Самара: Новая техника, 2018. - С.2534-2545.
Аннотация : Сardiovascular diseases are one of the leading causes of death worldwide. People suffering from or at high risk of such diseases need constant supervision, early diagnosis and timely assistance. It is shown that the achievement of high accuracy performance in real-time arrhythmias recognition is associated with significant hardware costs. Detection accuracy of arrhythmias recognition does not exceed 80%. An approach, which is offered to solve the problem of high-precision arrhythmia diagnosis on the basis of electrocardiosignal is based on the data mining methods. Application of such methods is necessary for processing large amounts of data with complex structure of the features. Determination of the arrhythmia type with the use of fuzzy inference tools needs to specify the technique of the original data preprocessing. Feature selection, formalization and coding is considered in this paper. The issue of the knowledge base construction – coding, generation and selection of the features (database) as well as the construction of the rules base – as the part of the neuro-fuzzy diagnostic system is also considered. The research goal is to improve the intelligent systems of arrhythmia diagnostics on the basis of neural network classifiers by developing a solution explanation subsystem based on neuro-fuzzy models.
Описание : Основная статья
URI (Унифицированный идентификатор ресурса) : http://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Development-of-the-Structure-of-the-Knowledge-Base-for-NeuroFuzzy-Diagnostic-System-69647
Другие идентификаторы : Dspace\SGAU\20180518\69647
Располагается в коллекциях: Информационные технологии и нанотехнологии

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