Development of estimation and forecasting method in intelligent decision support systems

Mahdi, Qasim Abbood; Shyshatskyi, Andrii; Prokopenko, Yevgen; Ivakhnenko, Tetiana; КУПРІЄНКО, Дмитро; Golian, Vira; Lazuta, Roman; Kravchenko, Serhii; Protas, Nadiia; Momit, Alexander

doi:10.15587/1729-4061.2021.232718

Cited by 29 publications

(63 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study is a further development of research aimed at developing methodological principles for improving the efficiency of information and analytical support, published earlier [2,[4][5][6]23].…”

Section: Discussionmentioning

confidence: 99%

“…1). It should be noted that the main procedures of the proposed method are described in detail in [23]. A distinctive feature of this method is that at step 6, the genetic algorithm improved by the authors is used, the main stages of which are given in Fig.…”

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

“…At this stage, the type of uncertainty about the analysis object state is taken into account and the basic model of the object state is initialized [22][23][24][25].…”

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

“…End of the algorithm. Procedures (1)-( 5), ( 7)- (10) of the proposed method are described in detail in [23].…”

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Development of object state evaluation method in intelligent decision support systems

Zhuravskyi

Sova

Коробченко

et al. 2021

EEJET

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

“…At this stage, the type of uncertainty about the analysis object state is taken into account and the basic model of the object state is initialized [22][23][24][25].…”

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

“…End of the algorithm. Procedures (1)-( 5), ( 7)- (10) of the proposed method are described in detail in [23].…”

Section: Algorithm For Implementing the Evaluation Methnod In Intelligent Decision Support Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Development of object state evaluation method in intelligent decision support systems

Zhuravskyi

Sova

Коробченко

et al. 2021

EEJET

Self Cite

View full text Add to dashboard Cite

show abstract

“…Fig. 1 presents a block diagram of an object state analysis and forecasting control system, which is divided into [11,[30][31][32]:…”

Section: Formalized Description Of the Problem Of Analyzing And Forecasting The Objects Statementioning

confidence: 99%

Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

Abed

Репіло

Zhyvotovskyi

et al. 2021

EEJET

Self Cite

View full text Add to dashboard Cite

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of 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 procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

show abstract

Implementation of Intelligent Decision Model of Traditional Hanfu 3D Design Based on Internet of Things

Wang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In view of the problems existing in the design and application of traditional Hanfu, in order to better promote the application prospect of the Internet of Things in traditional Hanfu, the adaptive estimation model is adopted to analyze the relevant algorithms. Based on the Internet of Things theory, the optimized intelligent decision model is obtained, and the relevant parameters of the model are modified so as to obtain the application of traditional Hanfu in 3D simulation design based on the Internet of Things theory. Finally, the optimized model is used to predict and analyze the design and application of traditional Hanfu. The results show that the value of the Internet of Things in 2021 is 880, an increase of about 8.6 times compared with 102 in 2011, indicating that the industry shows a strong growth trend. In addition, the growth rate of the Internet of Things from 2018 to 2021 is low, indicating that the output value of the Internet of Things in this stage is relatively stable, and it also reflects from the side that the Internet of Things has entered a stable growth period. With the increase in the number of iterations, the corresponding curves of the two original trust degrees show little change, and the original trust degree curves are mainly divided into the stage of slow rise and the stage of steady increase. The influence of the optimization model on the willingness to trust is obvious, showing varying degrees of increase. Different calculation methods describe the test data differently. The mathematical estimation method shows an approximate linear trend. The result estimation method shows the change in the quadratic function. The key decision rules have the characteristic of subsection. Data method has strong fluctuation in the early stage. Objective function method can better describe the characteristics of traditional Hanfu. This study realizes the design and application of traditional Hanfu in the field of 3D simulation based on the Internet of Things theory so as to provide a theoretical basis for the application and research of the Internet of Things in other fields.

show abstract

Development of estimation and forecasting method in intelligent decision support systems

Cited by 29 publications

References 19 publications

Development of object state evaluation method in intelligent decision support systems

Development of object state evaluation method in intelligent decision support systems

Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

Implementation of Intelligent Decision Model of Traditional Hanfu 3D Design Based on Internet of Things

Contact Info

Product

Resources

About