Research on risk evaluation of internet strategic transformation of manufacturing enterprises based on bp artificial neural network

2021 ◽  
pp. 1-8
Author(s):  
Huang Honglei ◽  
Bai Jiaming ◽  
Liu Tong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Risk Evaluation ◽  
Industrial Revolution ◽  
Evaluation Index System ◽  
Bp Artificial Neural Network ◽  
Manufacturing Enterprises ◽  
Strategy Transformation ◽  
Artificial Neural ◽  
Strategic Transformation

For manufacturing enterprises to successfully enter the “Industry 4.0” era and establish advantages in the new wave of industrial revolution, they must use Internet thinking to transform manufacturing enterprises and promote the in-depth integration of informatization and industrialization under the premise of managing and controlling risks, to achieve transformation and upgrading. The innovation and management of the risks of manufacturing enterprises’ Internet strategic transformation directly affects the success or failure of enterprises’ transformation. On the basis of constructing the risk evaluation index system of manufacturing enterprise’s Internet strategy transformation, a manufacturing enterprise’s Internet strategy transformation risk evaluation model was constructed based on BP artificial neural network, and typical enterprises were selected to carry out practical application. The results show that the BP artificial neural network structure of the risk evaluation of the Internet strategic transformation of the manufacturing enterprises includes 18 layers of network input layer, 4 nodes of network hidden layer and 4 neurons of output layer, and the application effect of model is good.

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

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

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