Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply

2008 ◽  
Vol 392-394 ◽  
pp. 735-742
Author(s):  
Bo You ◽  
De Li Jia ◽  
Feng Jing Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Power Supply ◽  
Fuzzy Neural Network ◽  
Cutting Process ◽  
Experimental Result ◽  
Plasma Arc ◽  
Cutting Power ◽  
Plasma Arc Cutting ◽  
Fuzzy Neural

A variable interval fuzzy quantification algorithm with self-adjustable factor in full domain is proposed in this paper. It focuses on digital inverted plasma arc cutting power and studies strong nonlinearity and uncertainty of power. The neural network is also introduced to decouple cutting parameters variables in the multi-parameters coupling cutting process. This algorithm avoids complex nonlinear system modeling and realizes real-time and effective online control of cutting process by combining advantages of fuzzy control and neural network control. Furthermore, the optimized fuzzy control improves steady-state precision and dynamic performance of system simultaneously. The experimental result shows that this control improves precision, ripples, finish and other comprehensive index of work piece cut, and plasma arc cutting power supply based on fuzzy-neural network has excellent control performance.

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.

Irrigation Water Compensation Control System Based on Fuzzy Neural Network

2010 ◽  
Vol 108-111 ◽  
pp. 1386-1391
Author(s):  
Bo Wu ◽  
Bang Yan Ye ◽  
Chun Ling Wu ◽  
Cheng Zhong Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Irrigation Water ◽  
Fuzzy Neural Network ◽  
Experimental Result ◽  
Data Sampling ◽  
Water Control ◽  
Sampling Device ◽  
Fuzzy Neural ◽  
Water Compensation

This paper presents a Fuzzy Neural Network-based irrigation water control system for realizing accurate irrigation and water saving. The control system consists of a PC based server with a FNN-based control module, data sampling device, CAN bus communication network and remote irrigation units with sensors and controller. It can estimate irrigation water compensation according to the irrigation environments. By using Fuzzy Logic function and Fuzzy Neural Network, the controller module can fuse multi-source information that from Internet, sensors and other sources to make decision of irrigation water compensation on the control spot. Experimental result indicates that the compensation generated by FNN have higher precision than that by FL in this irrigation water control system.

Study on Fuzzy Data Fusion for Real-Time Intelligent Recognition of Tool Wear State

2008 ◽  
Vol 375-376 ◽  
pp. 626-630
Author(s):  
Bang Yan Ye ◽  
Jian Ping Liu ◽  
Rui Tao Peng ◽  
Yong Tang ◽  
Xue Zhi Zhao
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Experimental Result ◽  
Fuzzy Data ◽  
Sample Classification ◽  
Fuzzy Neural ◽  
To Come

For detecting gradual tool wear state on line, the methods of Wavelet Fuzzy Neural Network, Regression Neural Network and Sample Classification Fuzzy Neural Network by detecting cutting force, motor power of machine tool and AE signal respectively are presented. Although these methods are not difficult to come true and processed accurately and rapidly, it is difficult to obtain comprehensive information of machining and exact value of tool wear when using single method of intelligent modeling and single signal detecting. For this purpose, fuzzy inference technique is adopted to fuse the recognized data. Emulation experiment is carried out by using Matlab software platform and this method is verified to be feasible. Experimental result indicates that by applying fuzzy data fusion, we can get an exact tool wear forecast rapidly.

Study of synthesis identification in the cutting process with a fuzzy neural network

2002 ◽  
Vol 129 (1-3) ◽  
pp. 131-134 ◽  
Author(s):  
B Lin ◽  
M.Z Zhu ◽  
S.Y Yu ◽  
H.T Zhu ◽  
M.X Lin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Cutting Process ◽  
Fuzzy Neural

Research of Test Model for Eggshell Crack Detection

2013 ◽  
Vol 846-847 ◽  
pp. 655-658
Author(s):  
Jian Yang ◽  
Chun Yan Xia ◽  
He Pan ◽  
Ying Shi ◽  
Xiu Ying Li
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Experimental Result ◽  
Test Model ◽  
Inference Rules ◽  
Characteristic Parameters ◽  
Fuzzy Neural ◽  
Set Up

In order to realize the precise identification of eggshell crack, we design eggshell cracks detection method based on image processing and fuzzy neural network. Firstly this method gets two pieces image of eggs and processes, and then counts number of the same gray pixel. Determine five characteristic parameters as the input of fuzzy neural network. Set up a fuzzy neural network. Its structure is 5-10-1. Eggshell cracks and noise in egg images were distinguished using automatically learning and inference rules of fuzzy neural network. Use 147 groups of parameters for training network and rest 58 sample for verifying. Experimental result shows that the model can meet actual testing requirements with fast, stable, high precision and good robustness, easy to implement. Its precision reached 94.55%.

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.

Fuzzy Neural Network Based on Genetic Algorithm for Temperature Control of Variable Air Volume Air Conditioning

2014 ◽  
Vol 599-601 ◽  
pp. 952-955
Author(s):  
Jie Jia Li ◽  
Yong Qiang Chen ◽  
Xiao Yan Han
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Air Conditioning ◽  
Fuzzy Neural Network ◽  
Learning Ability ◽  
Variable Air Volume ◽  
Air Volume ◽  
Fuzzy Neural

In this paper, the theory of the fuzzy control and self-learning ability of neural network is combined, joining the genetic algorithm to optimize the fuzzy control rules, so in the light of temperature control system of variable air volume air conditioning puts forward a fuzzy neural network control method based on genetic algorithm,and this paper introduces in detail the structure, algorithm of fuzzy control and neural network. In addition,this paper verifies the superiority of the fuzzy neural network based on genetic algorithm and ordinary fuzzy neural control.

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