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
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