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
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;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542678