Open AccessDevelopment of object state evaluation method in intelligent decision support systems
Author(s) -
Yurii Zhuravskyi,
Oleg Sova,
Сергій Олегович Коробченко,
Vitaliy Baginsky,
Yurii Tsimura,
Leonid Kolodiichuk,
Pavlo Khomenko,
Nataliia Garashchuk,
Olena Orobinska,
Andrii Shyshatskyi
Publication year - 2021
Publication title -
eastern-european journal of enterprise technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.268
H-Index - 24
eISSN - 1729-4061
pISSN - 1729-3774
DOI - 10.15587/1729-4061.2021.246421
Subject(s) - fuzzy cognitive map , computer science , fuzzy logic , artificial intelligence , machine learning , genetic algorithm , neuro fuzzy , object (grammar) , artificial neural network , cognition , population , data mining , fuzzy control system , demography , neuroscience , sociology , biology
Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method allows increasing the efficiency of data processing at the level of 16–24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.