An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
نویسندگان
چکیده
The dictionary learning algorithm has been successfully applied to electronic noses because of its high recognition rate. However, most algorithms use l0-norm or l1-norm regularize the sparse coefficients, which means that nose takes a long time test samples and results in inefficiency system. Aiming at accelerating speed system, an efficient is proposed this paper where performs multi-column atomic update. Meanwhile, solve problem singular value decomposition k-means (K-SVD) little discriminative power, novel classification model proposed, coefficient matrix achieved by linear projection training sample, constraint imposed coefficients same category should keep large be closer their class centers while different categories sparsity. was evaluated analyzed based on comparisons several traditional algorithms. When dimension sample larger than 10, average rate maintained above 92%, controlled within 4 s. experimental show improved effective method for development nose.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12136650