Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

2014 ◽  
Vol 1030-1032 ◽  
pp. 1574-1577
Author(s):  
Dao Kun Zhang ◽  
Rui Huo ◽  
Shu Ying Li ◽  
Xing Ke Cui ◽  
Cui Ping Liu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulated Data ◽  
Chatter Suppression ◽  
Cutting Chatter ◽  
Adaptive Ability ◽  
Neural Network Pid ◽  
Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

System of Annealing Furnace Temperature Control Based on Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 881-884
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nonlinear Approximation ◽  
Annealing Furnace ◽  
Furnace Temperature ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

In the thesis the adaptive ability of neural network strong and good nonlinear approximation ability, A controller is designed based on BP neural network by the adaptive ability of neural network strong and good nonlinear approximation ability in this paper, this method changed defect of the usual PID controller that parameters of annealing furnace condition are not easy set and the ability to adapt is poor. The new method is not only has good stability, but also has high control precision and strong adaptability.

scholarly journals Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

2018 ◽  
Vol 108 ◽  
pp. 042056
Author(s):  
Jinzhi Ren ◽  
Wei Xiang ◽  
Lin Zhao ◽  
Jianbo Wu ◽  
Lianzhen Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Neural Network Pid

Research on BP Neural Network Algorithm Based on Quasi-Newton Method

2014 ◽  
Vol 686 ◽  
pp. 388-394 ◽  
Author(s):  
Pei Xin Lu
Keyword(s):  
Neural Network ◽  
Empirical Analysis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Newton Methods ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quasi Newton ◽  
Improved Bp Algorithm

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.

Study for PID Temperature Control of Plant Growth Cabinet Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 229-232
Author(s):  
Zhi Qi Liu ◽  
Li Guo Tian ◽  
Meng Li ◽  
Jiang Lin Wei ◽  
Gao Li Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Plant Growth ◽  
Bp Neural Network ◽  
Pid Control ◽  
Response Speed ◽  
Fast Response ◽  
Control Methods ◽  
Target Temperature ◽  
Growth Environment ◽  
Neural Network Pid

Environment for plant growth is difficult to establish precise mathematical model. The conventional control methods are difficult to be well controlled, and put forward a neural network PID control temperature on the growth environment of plant. In this paper, taking the lettuce as an example, using MATLAB to simulate the PID control and PID control of BP neural network, the results proved that PID control of BP neural network has small overshoot, fast response speed and good stability compared with the traditional PID control, and better controlled temperature changing with the target temperature.

Research on the Improvement of BP Neural Network Algorithm and its Application

2014 ◽  
Vol 926-930 ◽  
pp. 3216-3219 ◽  
Author(s):  
Xiong Kai
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Simulation Calculation ◽  
Neural Network Algorithm ◽  
Working Principle ◽  
Calculation Ability ◽  
Improved Bp Algorithm

BP neural network has excellent ability to solve nonlinear optimal problem for its powerful simulation calculation ability and is wildly used in various disciplines in recent years, but its shortages of low convergence speed and falling into local minimum easily limit the usage of the algorithm. Based on the Levenberg-Marqardt optimization algorithm, the paper improves the BP neural network to overcome its shortages. First, the working structure and shortages of BP neural network are analyzed with more details. Second, the working principle of BP neural network algorithm is improved with Levenberg-Marqardt optimization algorithm and the calculation flows is redesigned. Third, data from three universities are used to realize the improved algorithm and the experimental results shows that the improved BP algorithm has better performance in calculation time and evaluation accuracy when used in university classroom teaching evaluation practically.

The Application Research of PLC Temperature Control System Based on Fuzzy Neural Network PID

2013 ◽  
Vol 340 ◽  
pp. 517-522
Author(s):  
Yong Wei Lu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Experimental Result ◽  
Temperature Control System ◽  
Pid Algorithm ◽  
Fuzzy Neural ◽  
Neural Network Pid

it is hard to establish the accurate model by using the traditional PID algorithm, and hard to adjust the system parameter in nonlinear system. In order to solve this problem, this paper proposed PID algorithm based on Fuzzy Neural Network. This algorithm combined PID algorithm, fuzzy control algorithm and neural network algorithm together, and formed one kind intelligent control algorithm. This paper designed and researched this algorithm, and applied in the PLC temperature control system. The experimental result indicated that the fuzzy neural network PID controller improved the controller quality, conquered some question such as variable parameter and nonlinear, and enhanced the systems robustness.

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