Intelligent Monitoring of Taijiquan Exercise Based on Fuzzy Control Theory

2021 ◽  
pp. 103859
Author(s):  
Shengnan Lu ◽  
Shilong Wang
Keyword(s):  
Fuzzy Control ◽  
Control Theory ◽  
Intelligent Monitoring

Fuzzy control based on genetic algorithm in intelligent psychology teaching system

2021 ◽  
pp. 1-9
Author(s):  
Xiaojia Pang ◽  
Yuwen Ning
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Control Theory ◽  
Classroom Teaching ◽  
Improved Genetic Algorithm ◽  
Group Function ◽  
Teaching Process ◽  
Teaching Management ◽  
Teaching System ◽  
Volume Group

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

Comparison of Digital PD and Fuzzy Control Theory on a Hydraulic Servosystem

1991 ◽  
Vol 24 (8) ◽  
pp. 111-114
Author(s):  
K. Kutlu ◽  
H. Güner
Keyword(s):  
Fuzzy Control ◽  
Control Theory
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