神经计算技术在通信网络中的性能

Junho Jeong
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引用次数: 1

摘要

本研究探讨了神经计算技术在通信网络中的应用,并基于错误率、延迟和吞吐量评估了它们的性能。结果表明,不同的神经计算技术,如人工神经网络(ann)、卷积神经网络(cnn)、循环神经网络(RNNs)、长短期记忆(LSTM)和生成对抗网络(gan),在提高性能的有效性方面具有不同的权衡。技术的选择将基于应用的特殊要求。研究还评估了不同通信网络架构的相对性能,并确定了在通信网络中应用不同技术的权衡和限制。研究表明,需要进一步的研究来探索技术的使用,例如深度强化学习;在通信网络中,并研究如何使用技术来提高通信网络的安全性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance of Neural Computing Techniques in Communication Networks
This research investigates the use of neural computing techniques in communication networks and evaluates their performance based on error rate, delay, and throughput. The results indicate that different neural computing techniques, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GANs) have different trade-offs in terms of their effectiveness in improving performance. The selection of technique will base on the particular requirements of the application. The research also evaluates the relative performance of different communication network architectures and identified the trade-offs and limitations associated with the application of different techniques in communication networks. The research suggests that further research is needed to explore the use of techniques, such as deep reinforcement learning; in communication networks and to investigate how the employment of techniques can be used to improve the security and robustness of communication networks.
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