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.

scholarly journals Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Jing Sun ◽  
Yuting Zhao ◽  
ChangQing Yang ◽  
Xuanlong Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Oil Sands ◽  
Back Propagation ◽  
Songliao Basin ◽  
Source Rocks ◽  
Western Slope ◽  
Oil Sand ◽  
Neural Network Method ◽  
Network Method

AbstractHigh-quality hydrocarbon source rocks are present in the upper Cretaceous layer in the western slope of the Songliao Basin. Oil and gas have accumulated in these rocks at the shallow edge of the basin, which has led to the formation of oil sand resources. This study uses the back-propagation (BP) neural network method to predict the distribution of oil sand reservoirs and is the first study of its kind in China. First, based on the basic data collected by core sample, well log and geochemical analyses, and the reasonable selection of samples, the cores are divided into mudstone, siltstone, fine sandstone, medium sandstone, and sand, according to lithology. Second, a three-layer BP neural network model is constructed with two hidden S-type layers and one linear output layer. Third, through a comparison of the effect of different numbers of training sessions of the sample data, we demonstrate that the accuracy of the model can be increased to 90% after training the network 100,000 times. Then, the log-derived data of rocks with unknown lithologies are input into the neural network to predict whether they contain oil sands. We show that the BP neural network method can predict the distribution of oil sand reservoirs in the target horizon of the study area, and the results are consistent with research results on the corresponding sand reservoirs and sedimentary facies. Thus, we conclude that it is feasible to use the BP neural network method to predict the distribution of oil sand reservoirs.

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

2008 ◽  
Vol 33-37 ◽  
pp. 1283-1288 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Fuzzy Neural

Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

Speech Recognization Based on Support Vector Machine

2012 ◽  
Vol 433-440 ◽  
pp. 7516-7521
Author(s):  
Ling Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Speech Recognition ◽  
Bp Neural Network ◽  
Speech Signal ◽  
Local Minimum ◽  
Support Vector ◽  
Neural Network Method ◽  
Network Method ◽  
Fast Classification

Aiming at the deficiency of the local minimum occurring in neural network used for speech recognition, the paper employs support vector machine (SVM) to recognize the speech signal with four different components. First, SVM is utilized to perform the speech recognition. Then, the results are compared with those obtained by the BP neural network method. The comparison shows that SVM effectively overcomes the local minimum existing in neural network and has the advantages of the accurate and fast classification, indicating that SVM looks feasible to recognize the speech signal.

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