BP Neural Network's Application in Glass Fiber Textile Machine Parameter Tuning

2011 ◽  
Author(s):  
Lihong Zhang ◽  
Shuqian Chen
Keyword(s):  
Glass Fiber ◽  
Parameter Tuning ◽  
Machine Parameter ◽  
Textile Machine

Ant Colony Algorithm’s Application of Textile Monitoring Image Recognition

2011 ◽  
Vol 328-330 ◽  
pp. 1701-1704
Author(s):  
Yang Lie Fu ◽  
Shu Qian Chen ◽  
Li Hong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Global Optimization ◽  
Glass Fiber ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Default Rule ◽  
Rule Based ◽  
Intelligent Search ◽  
Textile Machine ◽  
Surveillance Method

We can use video surveillance method to detection the weft in Glass fiber textile machine, avoids glass fiber weft bristling by contact weft detection sensor, Glass fiber dust damages to human health. Using Ant Colony algorithm of intelligent search, global optimization, robust, positive feedback, distributed computing, easy combination with other methods and other characteristics, resolve image segmentation, extraction monitor weft system in regional conditions, then use the default rule-based artificial intelligence reasoning in the region separately.

Ant Colony Algorithm Application In Glass Fiber Textile Machine Parameter Tuning

2012 ◽  
Vol 462 ◽  
pp. 71-76 ◽  
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen ◽  
Gui Zhi Bai
Keyword(s):  
Glass Fiber ◽  
Pid Control ◽  
Ant Colony Algorithm ◽  
Control Process ◽  
Parameter Tuning ◽  
Integral Function ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Control Parameters ◽  
Optimization Strategy

In glass fiber textile process, non-axis volume cloth drive motor with glass fabric volume increases, increasing the pressure on the drive shaft, moreover, because of cloth non-axis volume makes the pressure in the process of change is evident, that causes the motor load changing constantly, the traditional PID control system controller cannot timely tracking response. In order to solve the problem which the control parameters optimizes, improves the system performance, proposed a new Ant colony algorithm PID parameters optimization strategy, this solution can combine characteristics that Ant colony algorithm can fast find the most superior parameter solution stably and PID can precise adjustment. In the control process, taken the PID parameters as a colony of ants, used to control the absolute error integral function as the optimization objective, dynamically adjust the PID control parameters in the control process, so as to realize the PID parameters on-line tuning.

Electromagnetism-like algorithm for support vector machine parameter tuning

2013 ◽  
Vol 18 (10) ◽  
pp. 1985-1998 ◽  
Author(s):  
Aleksandar Kartelj ◽  
Nenad Mitić ◽  
Vladimir Filipović ◽  
Dušan Tošić
Keyword(s):  
Support Vector Machine ◽  
Parameter Tuning ◽  
Support Vector ◽  
Machine Parameter ◽  
Support Vector Machine Parameter

Research of Glass Fiber Textile Monitor Image Recognition Based on Neural Network

2013 ◽  
Vol 694-697 ◽  
pp. 1978-1982
Author(s):  
Shu Qian Chen ◽  
Yang Lie Fu
Keyword(s):  
Neural Network ◽  
Glass Fiber ◽  
Image Recognition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Textile Machine ◽  
Weft Fiber ◽  
The Stability ◽  
Monitor Image ◽  
Neural Network Input

Researched on weft fiber cut problems of glass fiber, improved the efficiency of textile production. Glass fiber textile machine is a major producer machine of glass fiber cloth. Textile machines weft detection usually uses the contact type in production, requires that the weft maintains certain pressure to the sensor. Using this method will cause glass fiber weft bristling, and will produce glass fiber floating dust. Damage to the textile machine and has the harm to the human body health. Used video surveillance method to detection the weft, image recognition and speed directly affects the stability of the system. This paper presented a detection methods of glass fiber textiles weft fiber cut based on neural network-based, selected multiple features which were directly related to the image with the weft as neural network input vector, through repeated training samples to remove tiny ripple effects which were caused by weft textile jitter, overcome the traditional method detection accuracy was not high. Experimental results show that this method can effectively avoid the weft jitter, making accurate detection of the weft fiber cut, and achieved satisfactory results.

scholarly journals Support vector machine parameter tuning based on particle swarm optimization metaheuristic

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Konstantinas Korovkinas ◽  
Paulius Danėnas ◽  
Gintautas Garšva
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Recognition Task ◽  
Parameter Tuning ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Machine Parameter ◽  
Swarm Optimization ◽  
Support Vector Machine Parameter

This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors’ works. They indicate that the proposed method can improve classification performance for a sentiment recognition task.

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