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.

本文针对基于人工智能的最优频率选择混合模型与算法，展开理论与实践维度的系统分析。本研究探讨了神经网络（Neural Networks）、遗传算法（Genetic Algorithms）与机器学习方法的集成应用路径，以提升信号传输质量与网络运行效率。研究重点聚焦无线电通信系统（Radio Communication Systems）中的高效频率管理、干扰水平抑制以及数据传输速率优化。混合算法（Hybrid Algorithms）的应用可有效增强电信系统（Telecommunication Systems）的稳定性与能源利用效率。本研究成果对移动通信（Mobile Communications）、数字网络（Digital Networks）及先进电信技术（Advanced Telecommunication Technologies）的发展具有显著的科学与实践价值。