Resources Distribution Model of Critical IT Infrastructure with Clear Parameters Based on the Particle Swarm Algorithm

Dorogyy, Yaroslaw; Doroha-Ivaniuk, Olena; Ferens, Dmytro

doi:10.15407/emodel.41.02.023

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…(4) Regression period This is the phase of extinction. The new network public opinion will replace the old one, and the old one will gradually fade out of the scope of public discussion over time [17]. However, the demise of online public opinion does not represent the end of the event, and the subsequent influence of the event will more or less cause positive or negative effects on the real society.…”

Section: Network Public Opinion Predictionmentioning

confidence: 99%

Network Public Opinion Prediction Based on Improved Particle Swarm Optimization and BP Neural Network

2022

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The prediction of network public opinion can help to understand the development law and progress direction of network public opinion. It is helpful for relevant departments to deal with public opinion in a timely manner and has important value for the benign development of social public opinion. In order to provide a more effective forecasting method, this paper introduces the sample data and parameter setting of the prediction model on the basis of discussing the related technology of prediction model establishment. Finally, the IPSA-BP combined prediction model designed in this paper was compared with IPSA and BP model on the prediction results of five kinds of public opinion events. Experimental data showed that the error between the prediction results of IPSA-BP and the real value was about 2%. However, the error between the prediction results of IPSA and BP model and the real value is about 5%, so it is verified that the prediction ability of IPSA-BP has achieved the expected test effect.

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Section: Network Public Opinion Predictionmentioning

confidence: 99%

Network Public Opinion Prediction Based on Improved Particle Swarm Optimization and BP Neural Network

2022

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Design of Aero-engine Start-Up Fuel Control System Based on Particle Swarm Algorithm

Wang

Chen³

2023

Lecture Notes on Data Engineering and Communications Technologies

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Dynamic Particle Swarm Optimization in Ecotourism Comprehensive Evaluation Index System

Yan¹,

Dong²

2022

2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs)

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Resources Distribution Model of Critical IT Infrastructure with Clear Parameters Based on the Particle Swarm Algorithm

Cited by 5 publications

References 8 publications

Network Public Opinion Prediction Based on Improved Particle Swarm Optimization and BP Neural Network

Network Public Opinion Prediction Based on Improved Particle Swarm Optimization and BP Neural Network

Design of Aero-engine Start-Up Fuel Control System Based on Particle Swarm Algorithm

Dynamic Particle Swarm Optimization in Ecotourism Comprehensive Evaluation Index System

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