Published May 14, 2026 | Version v1
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HYBRID MODELS AND ALGORITHMS FOR SELECTING OPTIMAL FREQUENCIES BASED ON ARTIFICIAL INTELLIGENCE

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This article analyzes the theoretical and practical aspects of hybrid models and algorithms based on artificial intelligence for selecting optimal frequencies. The study examines the integration of neural networks, genetic algorithms, and machine learning methods to improve signal transmission quality and network efficiency. Particular attention is paid to effective frequency management in radio communication systems, reduction of interference levels, and optimization
of data transmission speed. The use of hybrid algorithms makes it possible to enhance the stability and energy efficiency of telecommunication systems. The research findings are of significant scientific and practical importance for the development
of mobile communications, digital networks, and advanced telecommunication technologies.

the integration of neural networks, genetic algorithms, and machine learning methods to improve signal transmission quality and network efficiency. Particular attention is paid to effective frequency management in radio communication systems, reduction of interference levels, and optimization
of data transmission speed. The use of hybrid algorithms makes it possible to enhance the stability and energy efficiency of telecommunication systems. The research findings are of significant scientific and practical importance for the development
of mobile communications, digital networks, and advanced telecommunication technologies.

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References

  • 1.Haykin S. Neural Networks and Learning Machines. – New York: Pearson, 2009. – B. 35–78.
  • 2.Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. – Boston: Addison-Wesley, 1989. – B. 50–120.
  • 3.Rappaport T.S. Wireless Communications: Principles and Practice. – New Jersey: Prentice Hall, 2002. – B. 112–180.
  • 4.Goodfellow I., Bengio Y., Courville A. Deep Learning. – Cambridge: MIT Press, 2016. – B. 145–210.
  • 5.Bishop C.M. Pattern Recognition and Machine Learning. – Springer, 2006. – B. 200–260.