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Relevance. Nowadays, no state in the world is able to work on the creation and implementation of artificial intelligence in isolation from others. Artificial intelligence technologies are actively used to solve both general and highly specialized tasks in various spheres of society. In the process of assessing (identifying) the state of complex and objects of analysis and management, there is a high degree of a priori uncertainty regarding their state and a small amount of initial data describing them. At the same time, despite the huge amount of information, the degree of non-linearity, illogicality and noisy data is increasing. That is why the issue of improving the efficiency of assessing the condition of complex and objects is an important and urgent issue. The object of research is the objects of analysis. The subject of the research is the identification and forecasting of the analysis objects state with the help of bio-inspired algorithms. In the research, the evaluation and forecasting method was developed using fuzzy cognitive maps and the genetic algorithm. The novelty of the proposed method consists in: taking into account the degree of uncertainty about the object state while calculating the correction factor; adding a correction factor for data noise as a result of distortion of information about the object state; reduction of computing costs while assessing the objects state; creation of a multi-level and interconnected description of hierarchical objects; adjusting the description of the object as a result of changing its current state using a genetic algorithm; the possibility of performing calculations with the original data, which are different in nature and units of measurement. It is advisable to implement the mentioned method in specialized software, which is used to analyze the state of complex technical systems and make decisions.
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