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<p>Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on their robust functionality. This study rigorously evaluates and enhances the reliability of computer networks by leveraging an index system and an evaluation model devised through neural networks. To strengthen network dependability, this research employs a Hopfield neural network to address multi-constraint Quality of Service (QoS) multicast routing challenges, thereby elevating network reliability. Simulation experiments demonstrate that the Hopfield neural network effectively mitigates network latency and exhibits superior convergence performance compared to conventional QoS multicast routing methods. Further, this paper applies the neural network-based evaluation model to analyze the reliability of the air traffic network within the H-area after integrating the optimized computer network framework. It is observed that the most significant contributor to traffic flow loss is the network's degree value. An analysis of traffic flow density, employing actual sector flow data, reveals that high traffic volumes typically precipitate congestion. Nonetheless, the traffic flow density value consistently exceeds 100, suggesting that the enhanced computer network model holds practical applicability in real-world scenarios.</p>
</abstract>

Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on their robust functionality. This study rigorously evaluates and enhances the reliability of computer networks by leveraging an index system and an evaluation model devised through neural networks. To strengthen network dependability, this research employs a Hopfield neural network to address multi-constraint Quality of Service (QoS) multicast routing challenges, thereby elevating network reliability. Simulation experiments demonstrate that the Hopfield neural network effectively mitigates network latency and exhibits superior convergence performance compared to conventional QoS multicast routing methods. Further, this paper applies the neural network-based evaluation model to analyze the reliability of the air traffic network within the H-area after integrating the optimized computer network framework. It is observed that the most significant contributor to traffic flow loss is the network's degree value. An analysis of traffic flow density, employing actual sector flow data, reveals that high traffic volumes typically precipitate congestion. Nonetheless, the traffic flow density value consistently exceeds 100, suggesting that the enhanced computer network model holds practical applicability in real-world scenarios.

Reliability assessment and optimization of computer networks based on neural networks

Department of Information Engineering of Zhujiang College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on...

Reliability assessment and optimization of computer networks based on neural networks

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 05 mar 2024

Przyjęty: 19 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1728

Słowa kluczowe
Neural network, Network reliability, QoS multicast routing, Network optimization

© 2024 Shijin Liu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Reliability assessment and optimization of computer networks based on neural networks

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 05 mar 2024

Przyjęty: 19 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1728

Słowa kluczoweNeural network, Network reliability, QoS multicast routing, Network optimization

© 2024 Shijin Liu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Neural network, Network reliability, QoS multicast routing, Network optimization