Development of estimation and forecasting method in intelligent decision support systems

• Main Authors: Іgor Romanenko, Andrii Golovanov, Vitalii Khoma, Andrii Shyshatskyi, Yevhen Demchenko, Lyubov Shabanova-Kushnarenko, Tetiana Ivakhnenko, Oleksandr Prokopenko, Oleh Havaliukh, Dmitrо Stupak
• Format: Article
• Language: English
• Published: PC Technology Center 2021-04-01
• Series: Eastern-European Journal of Enterprise Technologies
• Subjects: decision support systems; artificial neural networks; state forecasting; training
• Online Access: http://journals.uran.ua/eejet/article/view/229160
• ISSN: 1729-3774; 1729-4061

The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.