Data analytics-based decision support workflow for high-mix low-volume production systems

Gödri, István; Kardos, Csaba; Pfeiffer, András; Váncza, József

doi:10.1016/j.cirp.2019.04.001

Cited by 52 publications

(88 citation statements)

References 15 publications

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“…Then there is an aggregation based on the operation of the T-norm of the truth degrees of the rules preconditions: Then, activate the conclusion of the relevant rules in accordance with the degrees of truth of their assumptions based on the operation of implication (here, the implications of Mamdani operation of min-activation) [16,17]:…”

Section: Development Of Methods For Assessing and Forecasting The Electronic Environment In Intelligent Decision Making Support Systemsmentioning

confidence: 99%

Development of a method for assessment and forecasting of the radio electronic environment

et al. 2021

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Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

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Section: Development Of Methods For Assessing and Forecasting The Electronic Environment In Intelligent Decision Making Support Systemsmentioning

confidence: 99%

Development of a method for assessment and forecasting of the radio electronic environment

et al. 2021

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“…The work [21] presents the approach to processing data from various information sources. This approach allows processing data from various sources.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of estimation and forecasting method in intelligent decision support systems

Mahdi¹,

Shyshatskyi

Prokopenko

et al. 2021

EEJET

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The method of estimation and forecasting in intelligent decision support systems was developed. The essence of the method is the analysis of the current state of the object and short-term forecasting of the object state. Objective and complete analysis is achieved by using improved fuzzy temporal models of the object state and an improved procedure for processing the original data under uncertainty. Also, the possibility of objective and complete analysis is achieved through an improved procedure for forecasting the object state and an improved procedure for learning evolving artificial neural networks. The concepts of fuzzy cognitive model are related by subsets of influence fuzzy degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. The method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of the method is the possibility of taking into account the type of a priori uncertainty about the object state (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The possibility to clarify information about the object state is achieved using 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 their different time shifts relative to each other under uncertainty. The method provides an increase in data processing efficiency at the level of 15–25% using additional advanced procedures.

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“…The work [21] presented the approach to processing data from different sources of information. This approach allows processing data from various sources.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of estimation and forecasting method in intelligent decision support systems

Romanenko

Голованов

Khoma

et al. 2021

EEJET

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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.

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Data analytics-based decision support workflow for high-mix low-volume production systems

Cited by 52 publications

References 15 publications

Development of a method for assessment and forecasting of the radio electronic environment

Development of a method for assessment and forecasting of the radio electronic environment

Development of estimation and forecasting method in intelligent decision support systems

Development of estimation and forecasting method in intelligent decision support systems

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