scholarly journals Application of neural network technology to calculate well logging porosity on the example of UK2-7 formations in the Yelizarovsky deflection (Western Siberia)

2021 ◽  
Vol 266 ◽  
pp. 07005
Author(s):  
E.A. Lisovskaya ◽  
B.V. Platov
Keyword(s):  
Neural Network ◽  
Western Siberia ◽  
Oil And Gas ◽  
Well Logging ◽  
Network Technology ◽  
The West ◽  
The Neural Network ◽  
The Uk ◽  
Porosity Estimation ◽  
Neural Network Technology

The article describes the use of an artificial neural network to calculate porosity in the West Siberian oil and gas province for the UK2–7 strata. The estimated porosity was compared with core porosity data. Correlation coefficient between core samples porosity and well logging porosity (using the neural network) showed higher values in comparison with traditional methods of porosity estimation.

scholarly journals The use of the Neural Network in Predicting a Number of Diseases of the Gastrointestinal Tract caused by Parasites

2020 ◽  
pp. 9-19
Author(s):  
U. N. Musevi ◽  
K. S. Pashayeva ◽  
N. T. Abdullayev
Keyword(s):  
Neural Network ◽  
Gastrointestinal Tract ◽  
Functional State ◽  
Disease Prediction ◽  
Network Technology ◽  
Test Results ◽  
The Neural Network ◽  
Neural Network Technology ◽  
Inputs And Outputs

Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing diseases as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of training the network. For the created neural network, test results for the corresponding symptoms and disease prediction results for these symptoms were obtained.

A New Control Strategy for the STATCOM Based on Multi-Model Neural Network PI Controller

2013 ◽  
Vol 448-453 ◽  
pp. 2171-2174
Author(s):  
Chen Zhou ◽  
Yi Hui Zheng ◽  
Gang Yao ◽  
Li Xue Li ◽  
Xin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Factor ◽  
Control Strategy ◽  
Full Range ◽  
Pi Controller ◽  
Impact Loads ◽  
Network Technology ◽  
Static Synchronous Compensator ◽  
The Neural Network ◽  
Neural Network Technology

Aiming to dealing with the problems of power factor compensation and the limitations of the conventional PI controller in the Static Synchronous Compensator (STATCOM), a new control strategy based on multi-model and neural network PI controller is proposed. This control scheme applied the multi-model and neural network technology to the PI controller to meet the accuracy and speed of the power factor compensation under different impact loads. Meanwhile, the neural network technology is used to tune the PI controller parameters values according to an optimal control law, which can meet the requirements of full range working conditions and optimality. Simulation experiments show that compared to the traditional PI controller, PI controller based on multi-model and neural network is proved to be better capable of adapting to the changes of impact loads with a higher compensating precision, which makes the power factor maintained at about 1 after compensation.

AN ASSSESSMENT OF THE IMPACT OF NEURAL NETWORK TECHNOLOGY ON AUTOMATIC ACTIVE SONAR CLASSIFIER DEVELOPMENT

1991 ◽  
Vol 05 (04) ◽  
pp. 645-654 ◽  
Author(s):  
THOMAS B. HALEY
Keyword(s):  
Neural Network ◽  
Development Process ◽  
Future Research ◽  
Network Technology ◽  
Neural Network Approach ◽  
Active Sonar ◽  
The Neural Network ◽  
Sonar Systems ◽  
The Impact ◽  
Neural Network Technology

We have spent the last several years investigating likely roles for neural network technology in the automatic active underwater sonar classifier development process, including exploration of both the improvement of in-service sonar systems and the creation of new ones. Currently, numerous classifier studies place emphasis on comparing class distinction performance figures of neural networks to those of traditional classifiers. We find that, for active sonar classifiers, this approach provides an incomplete and sometimes misleading assessment of the value of neural network technology. It is demonstrated that it is meaningful to divide the classifier development process into its phases and evaluate the influence that the neural network approach has on each phase. Conclusions are drawn on the potential impact of neural network technology on the active sonar classifier development field, which may help provide motivation for areas of future research.

scholarly journals Differential Diagnosis of Parasitic Diseases of the Gastrointestinal Tract Using Artificial Neural Networks

2021 ◽  
Vol 3 (5) ◽  
pp. 01-05
Author(s):  
U N Musevi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Gastrointestinal Tract ◽  
Parasitic Diseases ◽  
Disease Prediction ◽  
Network Technology ◽  
Test Results ◽  
The Neural Network ◽  
Neural Network Technology

Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing illnesses as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of its training. For the created neural network, test results for the respective symptoms and disease prediction results for these symptoms were obtained.

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