Advanced Search
WANG Bing, LIN Tao, CHEN Shan-ben. Knowledge Acquiring in Intelligent Detecting System for Lack of Weld[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2001, (3): 29-32.
Citation: WANG Bing, LIN Tao, CHEN Shan-ben. Knowledge Acquiring in Intelligent Detecting System for Lack of Weld[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2001, (3): 29-32.

Knowledge Acquiring in Intelligent Detecting System for Lack of Weld

More Information
  • Received Date: January 08, 2001
  • It is the most important to acquire knowledge in intelligent systems and it reflects the intelligence level of the system.To apply soft computingtools to acquire knowledge is a focus of researchers from all over the world.As a new soft computingtool,the rough set theory has enormous potential in applying and it has been widely applied in lots of fields such as pattern recognition,medical diagnosis,medical data analysis,image processing,quality control,fault diagnosis,data mining,process control and so on.In our detecting system,traditional methods could not satisfy requirements for the restricition of real conditions and factors of operators and intelligent methods must be adopted.In order to realize the intelligent detecting,how to dffectively acquire knowledge is the key problem.to apply the rough set theory to acquire a correlative elementary knowledge and then optimize the knowledge by neural networks can improve the efficiency of acquiring knowledge.The method of acquiring the correlative elementary knowledge with rough set theory in our intelligent detecting system is given in this paper.
  • Related Articles

    [1]YANG Yachao, QUAN Huimin, DENG Linfeng, ZHAO Zhenxing. Prediction method of welding machine parameters based on neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(1): 32-36. DOI: 10.12073/j.hjxb.2018390008
    [2]CHEN Yuquan, GAO Xiangdong. Neural network compensation for micro-gap weld detection by magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(10): 33-36.
    [3]WANG Dongsheng, YANG Bin, TIAN Zongjun, SHEN Lida, HUANG Yinhui. Process parameters optimization of nanostructured ZrO2-7%Y2O3 coating deposited by plasma spraying based on genetic algorithms and neural networks[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (3): 10-14.
    [4]LIU Lipeng, WANG Wei, DONG Peixin, WEI Yanhong. Mechanical properties predication system for welded joints based on neural network optimized by genetic algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (7): 105-108.
    [5]CHEN Zhenhua, SHI Yaowu, ZHAO Haiyan. Ultrasonic testing of spot weld based on spectrum analysis and artificial neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (10): 76-80.
    [6]DI Xinjie, LI Wushen, BAI Shiwu, LIU Fangming. Metal magnetic memory signal recognition by neural network for welding crack[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (3): 13-16.
    [7]WANG Yu, GAO Da-lu, LIAO Ming-fu, FENG Jing. A model of artificial neural network for optimizing technological parameter of friction welding of dissimilar material[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2005, (4): 33-36.
    [8]Yang Hai-lan, Cai Yan, Chen Geng-jun, Wu Yi-xiong. Principal component analysis based artificial neural networks for arc welding quality control[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (4): 55-58,64.
    [9]LEI Yu cheng, LIU Wei, CHENG Xiao nong. BP Neural Network Predicting Model for Aluminium Alloy Keyhole Plasma Arc Welding in Vertical Position[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2002, (6): 41-43.
    [10]Chen Shanben, Wu Lin, Wang Qilong, Liu Yuchi. A Fuzzy Inference-neural Network Control of Dynamic Process of Weld Bead Width in Pulse TIG Welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1997, (3): 159-165.

Catalog

    Article views (218) PDF downloads (51) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return