A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.
Abstract-This paper focuses on reducing the interference effect among input attributes. When training different attributes together, there may exist negative effect among them due to interference. To reduce the interference, input attributes are placed into different groups such that attributes with no interference with each other are placed in the same group. Two types of grouping strategies are examined in this paper, i.e. non-overlapping and overlapping. To further enhance the performance, multiple learners are employed to tackle different groups. Three integration methods i.e. voting, weighting and result-integration network (RIN) are examined.It turns out that the result-integration network has the best performance, followed by weighting and then voting. The ensemble approach can improve the performance of neural-network learning. Such an approach also can be employed with feature selection to further enhance the performance.
In this paper, pure "Sn" "O" _2, "Cu/Sn" "O" _2, ZnO and Cu/ZnO gas sensitive materials were synthesized by a simple hydrothermal reaction and used to prepare a gas sensor array. The morphological structure and composition of the synthesized materials were characterized using SEM and XRD, respectively. The sensor array was combined with the BP neural network algorithm optimized by the Sparrow Search algorithm (SSA-BPNN) and applied to effectively identify the types of mixed toxic gases in the room, including formaldehyde, ammonia and xylene. The combination of sensor array with optimized neural network algorithms achieved a good classification result for gas mixture and the classification accuracy can reach 93.45% for different classes of mixtures composed of three gases (formaldehyde, ammonia, and xylene). Therefore, the sensor array combined with the SSA-BP algorithm in this study has done a good work in the qualitative identification of ternary gas mixtures and has some application potential.
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