Balanda, AnatoliiAnatoliiBalanda0000-0003-0936-0929Микола ПогорецькийSerhieieva, DianaDianaSerhieievaHribov, MikhailMikhailHribovToporetska, ZorianaZorianaToporetska2025-10-282025-10-282020Balanda, A., Pohoretskyi, M., Serhieieva, D., Hribov, M., & Toporetska, Z. (2020). Applying Hopfield Neural Networks To Solve CSP Problems. International Journal of Advanced Trends in Computer Science and Engineering, 9(4), 5485–5489. https://doi.org/10.30534/ijatcse/2020/19094202010.30534/ijatcse/2020/190942020https://ir.library.knu.ua/handle/15071834/8332The article reviews methods based on the Hopfield neural network for solving CSP and FCSP problems. The first attempt to apply this type of neural network to solving the CSP problem was made by Hopfield himself, after which a number of modifications of the original algorithm took place. That is, all the methods presented in the article are modifications of each other and have developed consistently. Some characteristics of Hopfield network-based methods in comparison with other (non-neural network-based) algorithms and CSP solutions are also given. In the field of artificial intelligence, there is a class of combinatorial problems called CSP problems (Constraint satisfaction Problems). They are a powerful tool for solving practical problems that can be designed for many variables that are bound together by constraints.enCSPconstraint satisfaction problemneural networkHopfield neural networkСтаття