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Failure detection and classification of circular sheets through the methods of perceptron neural network, Lvq and neurofuzzy using matlab and fuzzytech software

Iraji, M. S ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICIAS.2010.5716170
  3. Publisher: 2010
  4. Abstract:
  5. In this article, I have tried to design an intelligent system which can separate and classify perfect and defective circular plates according to their size. After preprocessing, specifications of defects and size are determined through image processing, and finally, a system is proposed through perceptron neural networks methods, neuro fuzzy method, and Lvq to separate these products on basis of their size and defects. In the designing of this system, when input and its related intend is obvious before training network, perceptron neural networks give more exact results. If input and its related output have been clarified but the output have been related to some sub-inputs, lvq method is used; and finally, when input and its related output have been mace clear but value of input variables is continuous, neuro fuzzy method is used
  6. Keywords:
  7. Neural network ; Neuro fuzzy ; Specification of defect ; Circular plates ; Exact results ; Failure detection ; Industrial automation ; Input variables ; Neuro-Fuzzy ; Neuro-fuzzy methods ; Perceptron neural networks ; Products classification ; Training network ; Defects ; Image processing ; Imaging systems ; Intelligent systems ; Specifications ; Fuzzy neural networks
  8. Source: 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010, 15 June 2010 through 17 June 2010, Kuala Lumpur ; 2010 ; 9781424466238 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5716170