An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory

被引:0
|
作者
Melin, P [1 ]
Castillo, O [1 ]
机构
[1] Tijuana Inst Technol, Dept Comp Sci, Chula Vista, CA 91909 USA
关键词
D O I
10.1142/9789812702661_0093
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We describe in this paper a new hybrid intelligent approach for industrial quality control combining neural networks, fuzzy logic and fractal theory. We also describe the application of the neuro-fuzzy-fractal approach to the problem of quality control in the manufacturing of sound speakers in a real plant. The quality control of the speakers was done before by manually checking the quality of sound achieved after production [4]. A human expert evaluates the quality of sound of the speakers to decide if production quality was achieved. Of course, this manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation [8]. For this reason, it was necessary to consider automating the quality control of the sound speakers.
引用
收藏
页码:513 / 518
页数:6
相关论文
共 50 条
  • [21] Hybrid Intelligent System for Disease Diagnosis Based on Artificial Neural Networks, Fuzzy Logic, and Genetic Algorithms
    Al-Absi, Hamada R. H.
    Abdullah, Azween
    Hassan, Mahamat Issa
    Shaban, Khaled Bashir
    INFORMATICS ENGINEERING AND INFORMATION SCIENCE, PT II, 2011, 252 : 128 - +
  • [22] Intelligent Control Strategy of a Three-Phase PWM Rectifier Based on Artificial Neural Networks Approach and Fuzzy Logic Controller
    Jamma, Mustapha
    Barara, Mohamed
    Bennassar, Abderrahim
    Akherraz, Mohammed
    PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS 2016), 2017, 552 : 329 - 339
  • [23] A fuzzy-logic approach to industrial control problems
    David J. G. James
    Keith J. Burnham
    Artificial Life and Robotics, 1997, 1 (2) : 59 - 63
  • [24] Experiences with fuzzy logic and neural networks in a control course
    Jurado, F
    Castro, M
    Carpio, J
    IEEE TRANSACTIONS ON EDUCATION, 2002, 45 (02) : 161 - 167
  • [25] Intelligent industrial process control based on fuzzy logic and machine learning
    Zermane H.
    Kasmi R.
    International Journal of Fuzzy System Applications, 2020, 9 (01) : 92 - 111
  • [26] Intelligent Control of Industrial Compensating Devices Based on the Application of Fuzzy Logic
    Kryvyi Rih National University, Vitaly Matusevich 11, Kryvyi Rih
    50027, Ukraine
    CEUR Workshop Proc., (218-230):
  • [27] Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks
    Egrioglu, Erol
    Aladag, Cagdas Hakan
    Yolcu, Ufuk
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (03) : 854 - 857
  • [28] Intelligent adaptive control of robotic dynamic systems with a new hybrid neuro-fuzzy-fractal approach
    Castillo, Oscar
    Melin, Patricia
    Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 1999, : 879 - 883
  • [29] Intelligent adaptive control of robotic dynamic systems with a new hybrid neuro-fuzzy-fractal approach
    Castillo, O
    Melin, P
    18TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 1999, : 879 - 883
  • [30] Adaptive intelligent control of aircraft dynamic systems with a new hybrid neuro-fuzzy-fractal approach
    Melin, P
    Castillo, O
    INTELLIGENT TECHNIQUES AND SOFT COMPUTING IN NUCLEAR SCIENCE AND ENGINEERING, 2000, : 359 - 368