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Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

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Abstract

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.

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Acknowledgments

Prof. Dr. Adil Baykasoğlu is grateful to Turkish Academy of Sciences (TÜBA) for supporting his scientific studies.

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Authors and Affiliations

  1. Department of Industrial Engineering, Erciyes University, Kayseri, Turkey

    Lale Özbakır & Sinem Kulluk

  2. Faculty of Engineering, University of Gaziantep, 27310, Gaziantep, Turkey

    Adil Baykasoğlu

Authors
  1. Lale Özbakır
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  2. Adil Baykasoğlu
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  3. Sinem Kulluk
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Correspondence to Adil Baykasoğlu.

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Özbakır, L., Baykasoğlu, A. & Kulluk, S. Rule extraction from artificial neural networks to discover causes of quality defects in fabric production. Neural Comput & Applic 20, 1117–1128 (2011). https://doi.org/10.1007/s00521-010-0434-0

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  • DOI: https://doi.org/10.1007/s00521-010-0434-0

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