Abstract
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
Highlights
Decision support systems (DSS) are actively used in all spheres of human life.They are especially common when processing large data sets in databases, process forecasting, providing information support to decision-makers in the decision-making process.Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 4/3 ( 112 ) 2021Existing DSS are based on statistical and artificial intelligence methods, which collect, process, summarize information about the objects state and forecast their future state.The creation of intelligent DSS has become a natural continuation of the widespread use of conventional DSS
The analysis found that the approaches and methods of modern model theory in control systems, which allow linguistic approximation of mathematical models of cybernetic systems, are of particular interest
To verify the adequacy of the model, a “historical meth- number of components of the multidimensional time series; od” is proposed, the constructed FCM is applied to ι is the operator to account for the degree of awareness of similar situations if they occurred in the past and their dy- the object state; χ is the operator to account for the degree namics are known
Summary
Decision support systems (DSS) are actively used in all spheres of human life. They are especially common when processing large data sets in databases, process forecasting, providing information support to decision-makers in the decision-making process. In [5,6,7], complex methods of processing various data are considered, which increase the efficiency of data processing in decision support systems From these works [1,2,3,4,5,6,7,8], it can be concluded that ANN allow processing different types of data, adapting their structure to the type and volume of initial data, thereby increasing their own productivity. Nonlinear interaction of objects and processes, non-stochastic uncertainty, nonlinearity of interaction, partial inconsistency and significant interdependence of components These shortcomings can be eliminated by fuzzy cognitive maps. There is an urgent scientific task to develop a method for estimating and forecasting the monitoring object state in intelligent decision support systems using artificial neural networks and fuzzy cognitive models
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