ArticleComputational intelligence and neuroscience2022
Research on Urban National Sports Fitness Demand Prediction Method Based on Ant Colony Algorithm.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Sentiment mining of online comments of sports venues: Consumer satisfaction and its influencing factors.PloS one · 2025Article
- Analysis of College Course Scheduling Problem Based on Ant Colony Algorithm.Computational intelligence and neuroscience · 2022Article
- Mediating Effects of Psychological Capital and Work Passion Between Emergency Head Nurses' Leadership and Emergency Nurses' Capacity in Emergency Public Health Events.Public health nursing (Boston, Mass.)Article
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Authors and funding
2 authors.
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Abstract
With the development of competitive sports, the enthusiasm of the public to participate in sports has gradually increased. At present, almost all streets in the city have their own fitness places, which provide a lot of help for public fitness. However, the existing fitness venues are obviously insufficient, the venues are limited, relatively single, and the open-space area is insufficient, which cannot meet the needs of mass sports fitness. Based on this, this paper studies and analyzes the prediction of urban national sports fitness demand based on the ant colony algorithm. First, this paper analyzes the National Fitness Situation and the related research on demand forecasting and puts forward the use of the ant colony algorithm to realize demand forecasting. This paper expounds on the research methods and algorithms commonly used in demand forecasting. The ant colony algorithm is used to improve the fuzzy analysis. The urban national sports fitness demand is divided into six secondary indicators, and different tertiary indicators are divided under each secondary indicator. Through simulation analysis, it is confirmed that the improved algorithm proposed in this paper converges faster and finds the best path most. At the same time, the weight of the urban national sports fitness demand index is calculated.
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