期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2013
卷号：3
期号：3
页码：399-406
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas.DOI:http://dx.doi.org/10.11591/ijece.v3i3.2531
其他摘要：Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas. DOI: http://dx.doi.org/10.11591/ijece.v3i3.2531
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