Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

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.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

2012 ◽  
Vol 457-458 ◽  
pp. 531-535 ◽  
Author(s):  
Zeng Tao Xue ◽  
Zheng Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
System Structure ◽  
Cement Kiln ◽  
Control Loop ◽  
Neural Network Controller ◽  
Network Controller ◽  
Hysteresis Nonlinearity ◽  
Fuzzy Neural

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Study of Intelligent Prediction Control of Surface Roughness in Grinding

2007 ◽  
Vol 329 ◽  
pp. 93-98
Author(s):  
Ning Ding ◽  
Long Shan Wang ◽  
Guang Fu Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Prediction Control

A surface roughness intelligent prediction control system during grinding is built. The system is composed of fuzzy neural network prediction subsystem and fuzzy neural network controller. In the fuzzy neural network prediction subsystem, the vibration data are added to the inputs besides the grinding condition, such as feed and speed, so as to improve the dynamic performance of the prediction subsystem. The fuzzy neural network controller is able to adapt grinding parameters in process to improve the surface roughness of machined parts when the roughness is not meeting requirements. Experiment verifies that the developed prediction control system is feasible and has high prediction and control accuracy.

Temperature Controller of Heating Furnace Based on Fuzzy Neural Network Technology

2013 ◽  
Vol 748 ◽  
pp. 820-825
Author(s):  
De Quan Shi ◽  
Gui Li Gao ◽  
Ying Liu ◽  
Hui Ying Tang ◽  
Zhi Gao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Heating Furnace ◽  
Fuzzy Rules ◽  
Large Delay ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural

In this study, to solve the problem that heating furnace has the disadvantage of non-linearity, time variant and large delay, a fuzzy neural network controller has been designed according to the combination of fuzzy control and neural networks. In this controller, not only can the reasoning process of neural network be described by the fuzzy rules, but also the fuzzy rules can be dynamically adjusted by the neural network. In addition, the learning algorithm of the fuzzy neural network controller is studied. Simulation results show that the fuzzy neural network controller has good regulating performance and it can meet the needs of heating furnace during industrial production.

A Novel Greenhouse Control System Based on Fuzzy Neural Network

2014 ◽  
Vol 668-669 ◽  
pp. 415-418 ◽  
Author(s):  
Xin Yi Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Control Strategies ◽  
Control Process ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Greenhouse Control

Due to the complexity of greenhouse environment, greenhouse system cannot be controlled perfectly by traditional control method. This paper proposes a novel greenhouse control system based on fuzzy neural network to regulate the internal climate of the greenhouse. Temperature and humidity are selected as the inputs of controller, while the skylight, sun-shade net, circulation fan, side windows, fuel heater, and micro-mist humidifier are selected as the outputs. After analyzing every situation that may occur in the control process and the corresponding control strategies, we obtain 35 control “IF-THEN” rules. Simulation results show that the fuzzy neural network controller have certain improvements than the conventional PID controller in the aspects of overshoot, stability and response time.

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