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Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence

In order to improve the effect of tourism demand forecast, the commercial development of the tourism industry, and the actual experience of users, this paper uses adaptive neural network technology to conduct tourism demand forecast analysis. Moreover, this paper improves the adaptive neural network...

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Detalles Bibliográficos
Autor principal: Wang, Liangliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789447/
https://www.ncbi.nlm.nih.gov/pubmed/35087579
http://dx.doi.org/10.1155/2022/3376296
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author Wang, Liangliang
author_facet Wang, Liangliang
author_sort Wang, Liangliang
collection PubMed
description In order to improve the effect of tourism demand forecast, the commercial development of the tourism industry, and the actual experience of users, this paper uses adaptive neural network technology to conduct tourism demand forecast analysis. Moreover, this paper improves the adaptive neural network algorithm so that it can handle multiple data for tourism demand forecast. After improving the algorithm, this paper employs the actual process of tourism demand forecast to construct a tourism demand forecast model based on adaptive neural network technology. After that, this paper combines travel time and space data analysis to determine the system's functional structure and network topology. Through experimental research, it can be seen that the tourism demand forecast model based on adaptive neural network technology proposed in this paper performs well in tourism demand forecast and meets the actual demand of modern tourism forecast.
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spelling pubmed-87894472022-01-26 Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence Wang, Liangliang Comput Intell Neurosci Research Article In order to improve the effect of tourism demand forecast, the commercial development of the tourism industry, and the actual experience of users, this paper uses adaptive neural network technology to conduct tourism demand forecast analysis. Moreover, this paper improves the adaptive neural network algorithm so that it can handle multiple data for tourism demand forecast. After improving the algorithm, this paper employs the actual process of tourism demand forecast to construct a tourism demand forecast model based on adaptive neural network technology. After that, this paper combines travel time and space data analysis to determine the system's functional structure and network topology. Through experimental research, it can be seen that the tourism demand forecast model based on adaptive neural network technology proposed in this paper performs well in tourism demand forecast and meets the actual demand of modern tourism forecast. Hindawi 2022-01-18 /pmc/articles/PMC8789447/ /pubmed/35087579 http://dx.doi.org/10.1155/2022/3376296 Text en Copyright © 2022 Liangliang Wang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Liangliang
Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title_full Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title_fullStr Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title_full_unstemmed Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title_short Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
title_sort tourism demand forecast based on adaptive neural network technology in business intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789447/
https://www.ncbi.nlm.nih.gov/pubmed/35087579
http://dx.doi.org/10.1155/2022/3376296
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