scholarly journals Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nan Zhao ◽  
Sang-Bing Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Tourism Development ◽  
Development Economic ◽  
Bp Artificial Neural Network ◽  
Relationship Analysis ◽  
Artificial Neural

Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

Prediction of the Cutting Depth of Abrasive Suspension Jet Using a BP Artificial Neural Network

2012 ◽  
Vol 500 ◽  
pp. 249-252 ◽  
Author(s):  
Xiao Jian Liu ◽  
Qian Qian Fan ◽  
Yan Xia Feng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Cutting Depth ◽  
Bp Artificial Neural Network ◽  
The Mean ◽  
Artificial Neural ◽  
Model Training ◽  
Abrasive Suspension

Abrasive suspension jet is a new embranchment of abrasive jet. In the cutting process of this jet, the suspension concentration is constant, so the cutting quality is more stable. In this paper, a prediction model based on a back-propagation (BP) artificial neural network is presented for predicting the cutting depth generated by abrasive suspension jet. In the application of the BP neural network, the mean error of the output in the model training is 0.01, the relatively discrepancy is below 8.70%. The modeling method based on the BP neural network is much more convenient and exact compared with traditional methods, and can always achieve a much better prediction effect. It is verified with experiments to be reasonable and feasible, and it is the better foundation for the future study of abrasive suspension jet.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

Prediction of 3‐month treatment outcome of IgG4‐DS based on BP artificial neural network

Oral Diseases ◽  
2020 ◽  
Author(s):  
Yanxiong Shao ◽  
Zhijun Wang ◽  
Ningning Cao ◽  
Huan Shi ◽  
Lisong Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Treatment Outcome ◽  
Bp Artificial Neural Network ◽  
Artificial Neural
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