Modeling and control based on a new neural network model

2001 ◽  
Author(s):  
Yongbing Quan ◽  
Huaguang Zhang ◽  
Lilong Cai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Modeling And Control ◽  
And Control

Intelligent Computing Based on Neural Network Model in Problems of Kinematics and Control of Parallel Robot

2018 ◽  
Author(s):  
Sergey Yuriyevich Khalapyan ◽  
Anton Igorevich Glushchenko ◽  
Larisa Alexandrovna Rybak ◽  
Elena Vladimirovna Gaponenko ◽  
Dmitry Ivanovich Malyshev
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Parallel Robot ◽  
Intelligent Computing ◽  
And Control

A mask recognition algorithm based on improved YOLO-V4 neural network (Preprint)

2021 ◽  
Author(s):  
Song Zhang ◽  
Shaoqiang Wang ◽  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Target Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Prevention And Control ◽  
Preventive Measures ◽  
Recognition Algorithm ◽  
Facial Features ◽  
And Control

BACKGROUND With the spread of the new crown virus, the wearing of masks as one of the effective preventive measures is getting more and more attention, and the behavior of not wearing a mask is likely to cause the spread of the virus, which is not conducive to the prevention and control of the epidemic. OBJECTIVE In this paper, a new neural network model is used to better recognize the facial features of people with exit masks. METHODS This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network that can solve this problem well. This paper integrates SE-Net and DenseNet network as the reference neural network of YOLO-V4 and introduces deformable convolution. RESULTS Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of mask detection to a certain extent. CONCLUSIONS The improved YOLO-V4 network proposed in this article has verified its feasibility and accuracy through experiments and has great value in use. Improving the YOLO-V4 network can help better respond to face recognition with masks in the epidemic. However, the model studied in this article focuses on accuracy and is slightly lacking in speed. The next step is to increase its speed based on ensuring accuracy and consider actual deployment and use.

Prediction of Airport Energy Consumption Using a Hybrid Grey Neural Network Model

2012 ◽  
Vol 608-609 ◽  
pp. 1252-1256 ◽  
Author(s):  
Jing Jie Chen ◽  
Chen Xiao ◽  
Wen Gao Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Consumption Data ◽  
Prediction And Control ◽  
And Control

Prediction and control of airport energy consumption plays an important role in promoting energy saving and emission reduction in the civil aviation industry. In view of the complexity and nonlinearity of energy consumption system, as well as a small number of airport energy consumption data, this study develops a hybrid grey neural network model, which organically combines GM (1, 1) model and BP neural network in parallel and series connections, on the basis of analysis of main prediction methods. With energy consumption data from one Chinese airport for the whole year 2010, this study analyzes and compares different prediction results using different models through matlab. It shows that the hybrid model has a better accurate prediction, and its prediction accuracy can be controlled within 7%.

Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle

2006 ◽  
Author(s):  
Jiaming Wu ◽  
Xiaohui Xiong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Network Model ◽  
Neural Network Model ◽  
Trajectory Control ◽  
Control Analysis ◽  
Time Step ◽  
Neural Network Controller ◽  
And Control

The hydrodynamic and control performances of a self-stable controllable underwater towed vehicle developed by South China University of Technology under different depth trajectory control operations are analyzed by means of a proposed hydrodynamic numerical model. The model is established based on LMBP algorithm of neural network theory. Training samples for the neural network model are provided from the experimental data of the vehicle prototype towing experiments conducted in a large-scale ship model towing tank under the manipulation of a depressing wing installed in the vehicle. After the LMBP model is established, a depth trajectory control system for the towed vehicle is designed in order to accomplish vehicle trajectory control. This system is mainly composed of tow parts: a neural network identifier based on genetic algorithm and a fuzzy neural network controller based on genetic algorithm simulated annealing. Hydrodynamic performances of the vehicle under various control operations can then be numerically simulated with the proposed LMBP model and the depth trajectory control system of the towed vehicle. In numerical simulation of trajectory control to the towed vehicle, deflection of the vehicle’s depressing wing is adjusted at every time step by the proposed control system to match the trajectory of the vehicle with a pre-designated one. The value of the deflection is taken as input parameter for the LMBP neural network model, trajectory and attitude behavior of the towed vehicle under the control manipulations can then be predicted by the LMBP model.

Constant Cutting-Power Control of Shearer Based on Neural Network Model Predictive Control

2013 ◽  
Vol 823 ◽  
pp. 340-344
Author(s):  
Yuan Hua Zhou ◽  
Hong Wei Ma ◽  
Hai Yan Wu ◽  
You Jun Zhao
Keyword(s):  
Neural Network ◽  
Power Control ◽  
Network Model ◽  
Predictive Control ◽  
Neural Network Model ◽  
Control Method ◽  
Cutting Power ◽  
Nonlinear Predictive Control ◽  
The Neural Network ◽  
And Control

To solve the problem of constant power control of shearer cutting machine, the nonlinear predictive control method based on Neural Network was proposed in this thesis. In the method, the cutting current was used to identify the cutting load, and the Neural Network was used to predict and control the traction speed. A Neural Network model was built by the current and speed to control the cutting power of shearer. In MATLAB, the field data was used to simulate and the simulation verify the proposed scheme is better than PID method.

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