Apple grading based on IGWO optimized support Vector Machine

  • Zhao Yi
    School of Electrical Engineering and Automation, Henan Polytechnic University
  • Liu Qunpo
    Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment
  • Zhao Yuxi
    Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment
  • Sheng Yueqin
    Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment

説明

In order to improve the accuracy of apple external quality classification based on support vector machine, an improved grey wolf optimization algorithm IGWO was proposed by adding Logistic chaos mapping, nonlinear convergence factor and Cauchy variation to the grey wolf optimization algorithm. Firstly, different benchmark functions are used to test the improved IGWO algorithm. The test results show that the IGWO algorithm has improved the convergence speed and accuracy. Secondly, the image processing method is used to extract apple's external features as the data set. The improved grey wolf algorithm was used to optimize the penalty parameters and kernel parameters in support vector machine, and the optimal IGWO-SVM classification model was obtained. Finally, compared with the classification results of SVM and GMO-SVM, the results show that IGWO-SVM has the highest classification accuracy.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390859758187690624
  • DOI
    10.5954/icarob.2023.os8-3
  • ISSN
    21887829
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • Crossref
  • 抄録ライセンスフラグ
    使用不可

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