Applications of Computational Intelligence for Modeling, Identification, Optimization of Control Information Systems and Decision Support

The article presents a comprehensive approach to creating intelligent control systems for dynamic objects based on advanced computational intelligence methods. In the context of processing high-dimensional, unstable, and poorly formalized data in real-time, traditional modeling methods are insufficiently effective, which is especially critical for industrial and transport systems where control errors can lead to significant losses. The research is aimed at developing adaptive closed-loop control contours that combine current state identification, prediction, and the selection of optimal control influence under uncertainty. The proposed architecture integrates modules for data collection and processing, parameter identification, optimization, and decision support. The modeling core relies on hybrid machine learning algorithms, specifically a combination of Convolutional Neural Networks and Transformer architectures, as well as metaheuristic methods. Mechanisms for online re-training with minimal weight change and the use of Universal Transformer Memory are provided for adaptation to environmental changes. Remote parameter identification is realized by analyzing multimodal data streams, including video and sensor signals, using methods of object detection, scene segmentation, and optical flow analysis. The Decision Support System has a two-level structure: a rule-based level for operational processing of typical situations and an optimization level based on multi-criteria Pareto analysis. The use of Explainable AI ensures the transparency of decisions, and model adaptation is carried out using Reinforcement Learning. The results confirm the effectiveness of the approach in industrial, transport, and urban systems and outline the prospects for further development by incorporating multi-agent and neuromorphic technologies.