A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

Sensors

Attaphongse Taparugssanagorn¹  Pisanu Toochinda²  Prapun Suksompong³  Chalie Charoenlarpnopparut³  Mohammad Mizanur Rahman³ 

[1] ICT Department, Telecommunications, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand;School of Bio-chemical Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12121;School of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12121, Thailand;

关键词: electronic nose;    false alarm;    hyperspheric classification boundary;    classification;    correct rejection;   

DOI  :  10.3390/s17092089

来源: DOAJ

【 摘 要 】

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

【 授权许可】

Unknown