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A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter
2021
Mathematical Problems in Engineering
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Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. ...
IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced ...
Design of BP Neural Network PID Control Based on Improved GA 3.1. BP Neural Network PID Controller. ...
doi:10.1155/2021/6654997
fatcat:h52obikwcvakfdlwi2w2vy5pv4
Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations
2021
IET Renewable Power Generation
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ability of genetic algorithm and the adaptive adjustment characteristics of BP neural network. ...
Here, a genetic algorithm combined with BP neural network PID control (GA-BPPID) is proposed to improve both dynamic and anti-interference performances of Boost circuit by introducing the global optimization ...
ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China under project 52177171 and 51877040.
CONFLICT OF INTEREST No. ...
doi:10.1049/rpg2.12320
fatcat:lgixm3tuire5rfprxlh2etpk4q
Application of High-Speed Wind Tunnel Control Based on PID Neural Network
2011
Advanced Engineering Forum
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The BP neural network has been widely applied to the optimization of the PID controller parameter adjustment. ...
The PID neural network control system is introduced in the conventional PID control, which has advantages such as simple structure, physical meaning clear parameters, but also has a neural network of parallel ...
We have a architecture of PID control system based on BP neural network as shown in the Fig.2 . ...
doi:10.4028/www.scientific.net/aef.2-3.3
fatcat:txintqiazzcazjrvsrklfun2ea
A study of PID Neural Network Control Algorithm for Electric Gas Regulator
2016
International Journal of Control and Automation
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To improve the accuracy and reduce the response time of traditional gas regulator, a Proportional Integral Derivative (PID) neural network controller of electric gas regulator is proposed by combining ...
The neural network is demonstrated in a closed loop system of electric gas regulator and the ideal system output can be obtained by using this improved neural network algorithm. ...
The Structure of PID Control System based on the BP Neural Network The structure of the PID control system based on the BP neural network is shown in Figure 5 . ...
doi:10.14257/ijca.2016.9.11.20
fatcat:272ycgflu5a2tfwy2ngcd2rtdy
Research and Embedded Implementation of Let-off and Take-up Dynamic Control Based on Fuzzy Neural Network and Vector Control Optimization
2022
IEEE Access
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Then, based on fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. ...
FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). ...
The application of the GA-BP algorithm greatly improves the training speed and accuracy of FNN. ...
doi:10.1109/access.2022.3149838
fatcat:wsdek7xljfd7jgdvepbk6lqbi4
Process Control Optimization for Hydroelectric Power Based on Neural Network Algorithm
2017
Advances in Modelling and Analysis C
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the neural network and the fuzzy control theories and analyzes the BP and RBF network structures and identification simulation. ...
The fuzzy control rule for PID controller is optimized as a result the Fuzzy PID control may speed up the adjustment, and increase the speed at which the system tends to be stable, provided that the system ...
Conclusion This paper explores the application of the intelligent control theory based on the fuzzy control theory and the neural network theory in terms of identification and optimization of control parameters ...
doi:10.18280/ama_c.720204
fatcat:td6ykpsr4jfszaheycxnazmhsm
Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower
2021
Journal of Engineering Research and Reports
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This article introduces the basic concepts of distillation tower temperature control, comprehensively introduces the application of various neural network algorithms in distillation tower temperature control ...
At present, there are many researches on neural network control of distillation tower temperature. The methods are different and each has its own merits. ...
These neural network algorithms include PID neural network decoupling control, BP neural network-based control methods, RBF neural network-based control methods, fuzzy neural network-based control methods ...
doi:10.9734/jerr/2021/v20i417294
fatcat:krt6etrvbrg3rexrwyofkqajwu
Review of Control Strategies Employing Neural Network for Main Steam Temperature Control in Thermal Power Plant
2014
Jurnal Teknologi
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Neural network controls remains to be one of the most popular algorithm used to control main steam temperature to replace ever reliable but not so intelligent conventional PID control. ...
Self-learning nature of neural network mean the load on the control engineer re-tuning work will be reduced. ...
Neural Network intelligent PID control system based on immune GA and back-propagation (BP) neural network is discussed in 15 . ...
doi:10.11113/jt.v66.2488
fatcat:c7h3xxurifajfnnhgwd455hwky
A Review in Applications of Control Engineering Based on Genetic Algorithm
2022
Anbar journal for engineering sciences
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The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn't used till the time of preparing this review. ...
This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems. ...
Where, BP neural network coupled with a genetic algorithm by combining the global optimization capabilities of genetic algorithms with the adaptive adjustment properties of BP neural networks, PID control ...
doi:10.37649/aengs.2022.176356
fatcat:cugaot2n3bgrlggzwylvzopprq
Design and Analysis of Home Control Complex System Based on PLC Technology
2022
Mobile Information Systems
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First, the research significance and background of this study are discussed, and the complex system, traditional PID control, and BP neural network algorithm are summarized. ...
Second, the ZigBee wireless sensor network and PLC control system are studied, and the design of a smart health home control complex system based on PLC technology is analyzed. ...
e PID Control Algorithm Based on the BP Neural Network. e algorithm proposed in this study mainly combines the BP neural network control algorithm with the traditional PID control algorithm and makes up ...
doi:10.1155/2022/6830120
fatcat:cemogbjjmng3jmna7mshgze66i
Intelligent control of DC-DC converter based on PID-neural network
2019
International Journal of Power Electronics and Drive Systems
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Moreover, it has been noticed that the output voltage is more efficiently controlled when applying "PID-NN controller". ...
The results of the simulation show the efficiency of the suggested algorithm compared with other well-known learning methods. ...
Figure 3 shown of the "PID controller" block diagram based on neural network [18] . Figure 3 . The neural network based on controller of PID. ...
doi:10.11591/ijpeds.v10.i4.pp2254-2262
fatcat:t4d7vfagrzanvlpy6dt3pdn5xm
Constant Speed Control of Hydraulic Travel System Based on Neural Network Algorithm
2022
Processes
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Our analysis shows that the BP algorithm-based PID parameter self-tuning control method has no overshoot and that the three methods reduced the target speed tracking time by 90.11%, 75.12% and 36.55%, ...
, different control strategies were designed and compared with the state machine using the statechart module control, Z-N frequency response PID control, and GA-based PID parameter self-tuning methods. ...
When using a BP neural network to rectify the three parameters of the PID controller, it is necessary to determine the structure of the BP neural network, the initial values of the W ij and output layer ...
doi:10.3390/pr10050944
fatcat:xj6vy3aei5adnhlfy5ca3dwv3i
Modeling and PID control of quadrotor UAV based on machine learning
2022
Journal of Intelligent Systems
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Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. ...
In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. ...
Data availability statement: The datasets and stimuli of this study are available upon reasonable request from the corresponding author. ...
doi:10.1515/jisys-2021-0213
fatcat:xrrykflijzex3dm6gb6u6hnn7y
Design of advanced control experimental platform for main steam pressure based on Matlab
2018
IOP Conference Series: Materials Science and Engineering
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The subject of this thesis is to optimize PID parameters based on various optimization algorithms. ...
The purpose of cascade PID controller is to control the main steam pressure, simulate the control system by MATLAB and connect Simulink with GUI interface.The parameters are set in the interface, and all ...
BP neural network structure
3. ...
doi:10.1088/1757-899x/392/6/062178
fatcat:z6jlffwtdjgjjpvfafvqq4lpuy
Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning
2021
PLoS ONE
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(BP) neural network. ...
Methods Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with ...
Based on the analysis of the GABP algorithm, the GA is used to optimize the back-propagation neural network for weights and thresholds. ...
doi:10.1371/journal.pone.0254544
pmid:34265010
pmcid:PMC8282075
fatcat:kycodkonnfebdb2sillnaf6e7q
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