The Risk Evaluation Model of Construction Project Contract Based on BP Neural Network

2013 ◽  
Vol 357-360 ◽  
pp. 2304-2307
Author(s):  
Hua Liu ◽  
Jun Fang Yang ◽  
Zhi Yuan Zhang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Neural Network Method ◽  
Network Method ◽  
Indicator Model ◽  
Current Risk ◽  
Risk Evaluation Model

According to the current risk management of construction contract in China, put forward the idea of using neural network method to evaluate the risk in construction contract. Designed a comprehensive multi-indicator model for evaluating the construction contract risk based on BP Neural Network. This model can be used for simulating and evaluating the risk in construction contract in future. It has been proved that the desired results can be achieved by using this model.

Power Grid Risk Evaluation Model of Empty Mine Mined-Out Area Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 1710-1714
Author(s):  
Guo Feng Yang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Preventive Measures ◽  
Evaluation Model ◽  
Factors Associated ◽  
Bp Neural Network Model ◽  
Risk Evaluation Model

The risk evaluation of empty mine mined-out area is of great significance to the security and stability of the power facilities. But influence evaluation of empty mine mined-out area factor multitudinous, this paper selected seven factors associated with the system. Based on the principle of BP neural network, we build a 3 layer BP neural network model suitable for grid risk evaluation. The BP neural network model was trained by collected samples of empty mine mined-out area and the logical parameters of BP neural network were acquired and tested by the testing samples for accuracy, and finally we proposes preventive measures based on the evaluation.

Nursing Scheme Based on Back Propagation Neural Network and Probabilistic Neural Network in Chronic Kidney Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 416-421 ◽  
Author(s):  
Xuxia Ying ◽  
Bibo Tang ◽  
Canxin Zhou
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Bp Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Model Errors ◽  
Neural Network Method ◽  
Network Method ◽  
Case Data

Objective: The purpose of graded care for chronic kidney disease is to share expert experience, so that doctors can more accurately diagnose chronic kidney disease, so that patients with chronic kidney disease can understand their condition in time and collect case data. The collected case data is established into a data warehouse, the data quality is evaluated, and the BP neural network method is used for data mining to analyze the data. Methods: The paper studied BP neural network and probabilistic neural network (PNN), and used 75% of the samples to compare the models. The model errors were analyzed including maximum, minimum, expectation, variance and running time to get Adaboost. The accuracy and robustness of the -PNN model and the IGABP model are good. Results: BP neural network model and probabilistic neural network method can achieve higher application of graded care for chronic kidney disease. The method is capable of quickly predicting disease grading and providing a standardized treatment care regimen. The method realizes the main functions of querying, managing, and collecting data of medical records. Conclusion: The external expansion function of BP neural network and probabilistic neural network can achieve accurate data analysis, which can effectively improve the diagnosis time and grade prediction accuracy of chronic kidney disease, and provide opinions for graded nursing.

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