Development of object state evaluation method in intelligent decision support systems
| Autor: | Yurii Zhuravskyi, Oleg Sova, Serhii Korobchenko, Vitaliy Baginsky, Yurii Tsimura, Leonid Kolodiichuk, Pavlo Khomenko, Nataliia Garashchuk, Olena Orobinska, Andrii Shyshatskyi |
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| Rok vydání: | 2021 |
| Předmět: |
decision support system
Applied Mathematics Mechanical Engineering population Energy Engineering and Power Technology HD2321-4730.9 Industrial and Manufacturing Engineering Computer Science Applications Control and Systems Engineering Management of Technology and Innovation genetic algorithm T1-995 Industry Electrical and Electronic Engineering artificial neural networks Technology (General) |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 6, Iss 9 (114), Pp 54-63 (2021) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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