Improvement of Artificial Neural Network Algorithm and Its Application in Hydraulic Measurement Technology

Wu, Zhengnan; Yuansheng, Cheng

doi:10.1007/978-3-031-05237-8_99

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking Morden as an example, they believe that the positioning of small towns needs to consider a variety of factors, such as regional advantages, mature characteristic industries and cultural and creative industries, and local policy guidance. e authors (Vukovi ć Predrag, Č Avlin Gordana, and Č Avlin Miroslav) in various works proposed that the success of both rural tourism town in the UK benefits from natural hot spring resources, location conditions, and convenient transportation [16][17][18][19]. When studying the STP theory, (Daan Francois Toerien) found that the cultural and tourism towns have achieved good benefits in the innovative use of local characteristic resources [23,24].…”

Section: Related Workmentioning

confidence: 99%

Location Selection of Digital Cultural Tourism Town Based on Improved Genetic Algorithm and BP Neural Network

LeahWu

Qiao

Jia

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Due to the short development time of cultural and tourism towns in China, local governments and investors lack experience in building cultural and tourism towns and do not pay enough attention to the positioning of towns. Alternatively, this issue results in chaos in domestic cultural and tourism towns and even a large number of empty towns in some provinces. Therefore, how to accurately locate cultural tourism towns is a problem that must be studied in depth at present. This paper uses the regional economic theory to collect the influencing factors of cultural tourism town positioning. Based on the BP neural network and the improved genetic algorithm, a genetic neural network model is constructed to train and predict the samples of cultural tourism towns. Taking a small town in the East as a case, the data were collected and analyzed. Established on the prediction outcomes of the genetic neural network, the best location of a small town was selected according to the actual situation of the region. In terms of accuracy and training time, our experimental evaluation confirmed that the neural network enhanced by genetic algorithms outperforms the conventional BP neural network. Furthermore, we observed that besides the classification capabilities of the BP neural network-based model, the classical BP neural network improved by the genetic algorithm also exhibits great macrosearch capabilities and good global optimization performance.

show abstract