Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network

Wang, Han; Xu, Lingwei; Wang, Xianpeng

doi:10.3390/s19214789

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…Literature [17] selects four evaluation indexes as the factors influencing the spatial distribution of hotels, including economic development index (GDP, per capita GDP), tourism development index (number of inbound tourists, tourism foreign exchange income, number of domestic tourists, domestic tourism income, business development index (total retail sales of social consumer goods and total import and export) and transportation development index (turnover of goods, total foreign exchange income, and total domestic tourism income) and passenger turnover [18]. e factors related to the development scale of high-star hotels in the above literatures are total import and export, actually utilized foreign capital, number of foreign-funded enterprises, total retail sales of social consumer goods, total tourism income, total number of tourists, number of travel agencies, number of tourist attractions, number of international tourists, per capita public green space area, population (10000), urbanization level, GDP Per capita disposable income of urban residents, investment in fixed assets, per capita GDP, leading role of tertiary industry, passenger volume, transaction volume of exhibitions, and number of exhibitions [19].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Tsai

2021

Mathematical Problems in Engineering

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhao

Tsai

2021

Mathematical Problems in Engineering

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“…(3) In the reverse transmission, the weight of each layer unit is continuously modified until the output error is reduced to acceptable Or until the preset number of learning times [10][11]. Assumes that the input for , a unit of hidden layer, the output of , Output layer has n units, the output of , the target output for ,The transfer function from hidden layer to output layer is f, and the transfer function of output layer is h. The calculation formula is as follows:…”

Section: The Bp Neural Network Modelmentioning

confidence: 99%

An Analysis about the Pure Electric Vehicle Sales Prediction Based on the BP Neural Network

Yang

2023

AETR

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With the increasing demand for pure electric vehicles among Chinese residents, being able to accurately forecast the future sales volume of pure electric vehicles has become a hot topic of research in today’s society. In this paper, the sales volume of pure electric vehicles from January 2020 to December 2021 have been collected. And based on the monthly sales volume of 24 months, the BP neural network model has been used to test the data, and the sales volume of pure electric vehicles from January to December 2021 has been predicted with the trained model. Comparing its prediction results with the actual sales volume, it has been found that the BP neural network model can predict the future sales volume of electric vehicles well; and based on the prediction results, it can provide some useful references for the production planning and the raw material procurement.

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“…The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error backpropagation [33], including input layer, hidden layer, and output layer. At present, the research and application of the BP algorithm in various subjects develop rapidly.…”

Section: Back Propagation Neural Network Algorithmmentioning

confidence: 99%

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure

Liu

Che

Sun

et al. 2022

Sensors

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Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure’s key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.

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Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network

Cited by 6 publications

References 44 publications

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

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

An Analysis about the Pure Electric Vehicle Sales Prediction Based on the BP Neural Network

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure

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