Fuzzy control using neural network techniques

Author(s):  
T. Iwata ◽  
K. Machida ◽  
Y. Toda
Keyword(s):  
2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.


2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao

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.


2011 ◽  
Vol 18 (6) ◽  
pp. 785-795 ◽  
Author(s):  
Ken Yeh ◽  
Cheng-Wu Chen ◽  
DC Lo ◽  
Kevin FR Liu

2008 ◽  
Vol 35 (5) ◽  
pp. 473-486 ◽  
Author(s):  
Ricardo Bendaña ◽  
Alfredo del Caño ◽  
M. Pilar de la Cruz

This paper presents a fuzzy-logic-based system for selecting contractors. This tool, which was based on the fuzzy control technique, was created for the private sector client in traditional design–bid–build projects with one-step selection processes, but its philosophy can also serve other types of clients, industries, contracts, and selection processes. The system develops an assessment of different qualitative and quantitative issues that influence a contractor’s suitability for constructing a specific design in a specific environment (client’s needs and objectives, objectives prioritization, etc.), taking into account the risk of not achieving the client’s objectives. A computer application was developed and validated, including a Delphi analysis with professionals who are experts in contractor selection. The application covers the possibility of using different selection policies, when the essential project objective is cost, time or quality. As part of the validation process, a neural network was developed to prove that the fuzzy-control tool has a behavior that can be recognized by a neural network.


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