Temperature Control of Batch Reaction Based on Genetic Fuzzy Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1968-1971 ◽  
Author(s):  
Chun Tao Man ◽  
Jia Cui ◽  
Xin Xin Yang ◽  
Jun Kai Wang ◽  
Tian Feng Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Design Method ◽  
Batch Reactor ◽  
Batch Processes ◽  
Strong Nonlinearity ◽  
Control Scheme ◽  
Fuzzy Neural

The batch reactor has strong nonlinearity and hysteresis, the conventional control method is hard to meet the control requirements. According to the batch processes temperature control, this thesis proposed an intelligent control scheme. Combined neural networks with fuzzy logic control, searching and optimized parameters of fuzzy neural network by using Genetic Algorithm (GA), displayed the design method and optimization steps, and the simulation results verify the control scheme which proposed is feasible and effective.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

Wheeled Robot Based on Fuzzy Neural Network Tracking Studies

2013 ◽  
Vol 373-375 ◽  
pp. 181-184
Author(s):  
Su Ying Zhang ◽  
Shao Jie Xu ◽  
Jing Fei Zhu ◽  
Bing Hao Li ◽  
Wen Pan Shi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
External Disturbance ◽  
Network Control ◽  
Learning Ability ◽  
Parameter Uncertainties ◽  
Wheeled Robot ◽  
Fuzzy Neural ◽  
Arbitrary Precision

The wheeled robot with non-integrity constraints is a typical nonlinear system, in order to achieve the ideal path tracing, presented a theory based on fuzzy neural network control. Centralized compensation system based on neural network uncertainty can be arbitrary-precision approximation of continuous nonlinear functions as well as the complex uncertainties with adaptive and learning ability. By MATLAB simulation showed that the control method to ensure fast convergence and error robustness of parameter uncertainties and external disturbance.

Design of Temperature Control System Based on DSP and Fuzzy Neural Network

2013 ◽  
Vol 823 ◽  
pp. 384-387
Author(s):  
Ya Juan Chen ◽  
Yue Hong Zhang ◽  
Gen Wang Ying
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Good Control ◽  
Temperature Control System ◽  
Control Effect ◽  
Fuzzy Neural ◽  
Electric Boiler ◽  
Self Tuning

Using fuzzy neural network to tune PID parameters, and DSP as processor, it was designed that a set of electric boiler temperature control system based on PID parameters self-tuning, including the design of each hardware module and each software subroutine of the system. Experimental results show that compared with the traditional PID temperature control system, this temperature control system has the advantages such as good control effect, easy parameter adjustment, strong anti-jamming capability, better adaptability and robustness, has the feasibility and practical value.

scholarly journals Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin Zhang ◽  
Longhua Mu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Chaotic Oscillation ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

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