TY - GEN
T1 - Mixed odors classification by Neural Network using Radial Basis Function
AU - Faqih, Akhmad
AU - Krisnandhika, Bharasaka
AU - Putro, Benyamin Kusumo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - Odor research has become an attractive topic to be developed because of its potential application in advanced technologies. Thus, the classification of mixed odor becomes more important. But, classifying the mixed odors is difficult in getting high recognition. In this paper, we consider the three mixture of odor using 8 and 16 channels of sensor as main data and focusing on its classification method using neural network. The neural network used here are a Radial Basis Function Neural Network (RBFNN). K-mean clustering and Self-organizing map (SOM) are used for gaining the centers and standard deviations. We classify the three mixtures of odor into 18 and 12 classes for each data type. The result show high recognition which the accuracy rates using 16 channels of sensor are around 84.9% for 18 classes and around 93.4% for 12 classes, while the accuracy rates using 8 channels of sensor are around 88.2% for 18 classes and around 91.9% for 12 classes.
AB - Odor research has become an attractive topic to be developed because of its potential application in advanced technologies. Thus, the classification of mixed odor becomes more important. But, classifying the mixed odors is difficult in getting high recognition. In this paper, we consider the three mixture of odor using 8 and 16 channels of sensor as main data and focusing on its classification method using neural network. The neural network used here are a Radial Basis Function Neural Network (RBFNN). K-mean clustering and Self-organizing map (SOM) are used for gaining the centers and standard deviations. We classify the three mixtures of odor into 18 and 12 classes for each data type. The result show high recognition which the accuracy rates using 16 channels of sensor are around 84.9% for 18 classes and around 93.4% for 12 classes, while the accuracy rates using 8 channels of sensor are around 88.2% for 18 classes and around 91.9% for 12 classes.
KW - Classification
KW - Mixed odor
KW - Neural network
KW - Radial basis function
UR - http://www.scopus.com/inward/record.url?scp=85022346553&partnerID=8YFLogxK
U2 - 10.1109/ICCAR.2017.7942761
DO - 10.1109/ICCAR.2017.7942761
M3 - Conference contribution
AN - SCOPUS:85022346553
T3 - 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
SP - 567
EP - 570
BT - 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
Y2 - 22 April 2017 through 24 April 2017
ER -