计算机网络与人工神经网络的整合,打造基于人工智能的网络运营商

Binbin Wu, Jingyu Xu, Yifan Zhang, Bo Liu, Yulu Gong, Jiaxin Huang
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引用次数: 0

摘要

本文提出了一种结合计算机网络和人工神经网络的综合方法,以构建一个智能网络操作员,发挥人工智能模型的作用。计算机网络的状态信息被转化为嵌入式矢量,使操作员能够有效地识别不同的信息,并准确地为计算机网络的每个步骤输出适当的操作。操作员经过全面测试,准确率达到 100%,从而消除了操作风险。此外,还创建了一个简单的计算机网络模拟器,并将其封装为培训和测试环境组件,实现了数据收集、培训和测试过程的自动化。本摘要概述了论文的核心贡献,同时强调了在开发和验证基于人工智能的网络运营商时所采用的创新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of computer networks and artificial neural networks for an AI-based network operator
This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.
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