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.

An improved method of finding solutions based on the cuckoo algorithm is proposed. The research object is the decision-making support systems. The research subject is the decision making process in management tasks using artificial intelligence methods. The hypothesis of the research is to increase the efficiency of decision making with a given assessment reliability. The proposed method is based on a combination of the cuckoo algorithm and evolving artificial neural networks. The method has the following differences: ‒ an additional processing of the source data takes place taking into account the uncertainty about the state of the control objects and the type of data noise about the state of the control object is additionally taken into account; ‒ the state model of the control object is adjusted taking into account the available computing resources of the system; ‒ added procedures to reduce the probability of detecting nests and reducing the length of the cuckoo’s step; ‒ knowledge bases about management objects are additionally taught. 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 effectiveness of the proposed method was evaluated and it was established that the proposed modification provides a better value of the objective function compared to the results obtained by other authors and ensures the fulfillment of all restrictions. The specified example showed an increase in the efficiency of data processing at the level of 21–28 % due to the use of additional improved procedures. It is advisable to use the proposed method in decision making support systems of automated control systems.

In this study, the object of research is organizational and technical systems. The primary issue addressed is the enhancement of operational efficiency in assessing the state of such systems under constraints related to data reliability, regardless of the volume of incoming data acquired from information sources. The subject of the research is the process of evaluating the condition of organizational and technical systems. The study presents the development of a methodological approach for assessing the condition of complex organizational and technical systems. The originality of the proposed approach lies in the implementation of advanced auxiliary procedures that enable the following: – deployment of a search population of hippopotamus agents across the search plane, accounting for uncertainty in the information acquired through technical means about the organizational and technical system, by employing appropriate corrective coefficients. This enables a reduction in the time required for initial configuration of the subsystem responsible for processing heterogeneous data from extraction sources; – additional consideration of the velocity of each agent within the hippopotamus swarm, allowing the prioritization of search tasks by each individual agent within the corresponding search space (across elements and components of the organizational and technical system); – verification of the algorithm’s convergence to local and global optima; – replacement of ineffective search agents by refreshing the population of hippopotamus agents; – implementation of deep learning mechanisms for the knowledge bases of the hippopotamus agent swarm; – estimation of the required computational resources in cases where the available computational capacity is insufficient for performing the necessary calculations. An illustrative example demonstrated a 13–16 % increase in decision-making efficiency due to the integration of additional procedures, while ensuring a decision reliability level of 0.9

We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the computing resources of the device and taking into account the type and amount of information coming to the input of the artificial neural network. Another distinctive feature of the developed method is that no preliminary calculation data are required to calculate the input data. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, while making unambiguous decisions. According to the results of the study, this training method provides on average 10–18 % higher efficiency of training artificial neural networks and does not accumulate training errors. This method will allow training artificial neural networks by training the parameters and architecture, determining effective measures to improve the efficiency of artificial neural networks. This method will allow reducing the use of computing resources of decision support systems, developing measures to improve the efficiency of training artificial neural networks, increasing the efficiency of information processing in artificial neural networks.