• DocumentCode
    1711484
  • Title

    Classification of coffee using artificial neural network

  • Author

    Yip, Devil H F ; Yu, William W H

  • Author_Institution
    Dept. of Ind. & Manuf. Syst. Eng., Hong Kong Univ., Hong Kong
  • fYear
    1996
  • Firstpage
    655
  • Lastpage
    658
  • Abstract
    The paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive backpropagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has successfully classified the coffees using a relatively small set of training data. The performance of the optimized ANN compare significantly better than the methods proposed by other researchers
  • Keywords
    backpropagation; chemistry computing; gas sensors; genetic algorithms; neural nets; pattern classification; ANN topology; adaptive backpropagation; artificial neural network; coffee classification; genetic algorithm; optimal parameters; optimized ANN; training data; training process; Acceleration; Artificial neural networks; Genetic algorithms; Humans; Manufacturing industries; Network topology; Sensor arrays; Testing; Tin; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
  • Conference_Location
    Nagoya
  • Print_ISBN
    0-7803-2902-3
  • Type

    conf

  • DOI
    10.1109/ICEC.1996.542678
  • Filename
    542678