Intelligentization of decision-making processes in risk management systems based on neural networks of the art family

This article discusses the problems of using neural networks of the ART family to optimize the decision-making process in risk management systems. The advantages of this approach, such as the ability to quickly respond to new information and flexibility in learning, are weighed against disadvantages, including the difficulty of adjusting parameters and interpreting results. The next part of the article will explore various ways to train ART networks, including unsupervised learning and supervised learning methods, as well as key points for configuring network parameters. Possible problems related to the quality of input data and the difficulty of interpreting output data are raised. The article also presents a concrete example of the use of ART-type neural networks in the construction industry to assess risks and make informed decisions. In conclusion, the article focuses on the prospects for using neural networks of the ART family for cluster analysis of risks, identifying related factors and grouping them for more effective management. The possibilities for further development of decision-making methods in risk management using neural networks such as ART and their potential to provide more accurate and predictive practices are discussed.

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