Abstract
A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The 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 method also contains an improved procedure for processing initial data under a priori 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, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed Forces
Highlights
Decision support systems (DSS) are actively used in all spheres of human life
The aim of the study is to develop a method of object state estimation in intelligent decision support systems, which would allow for the analysis of the object state
The efficiency of the object state analysis process is chosen as an efficiency criterion of this method
Summary
Decision support systems (DSS) are actively used in all spheres of human life. They are especially common in the processing of large data sets in databases, process development forecasting, providing informational and analytical support to the decision-making process of decision-makers.Control processesExisting DSS are based on statistical and artificial intelligence methods, which provide collection, processing, generalization of information about the object (process) state and forecasting of future state.The creation of intelligent DSS has become a natural continuation of the widespread use of conventional DSS. Decision support systems (DSS) are actively used in all spheres of human life. They are especially common in the processing of large data sets in databases, process development forecasting, providing informational and analytical support to the decision-making process of decision-makers. Existing DSS are based on statistical and artificial intelligence methods, which provide collection, processing, generalization of information about the object (process) state and forecasting of future state. The main fundamental difference between intelligent and DSS is the presence of feedback and adaptability to changes in input processes [1, 2]. Intelligent DSS have been widely used to solve specific military tasks, namely [1, 2]:
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