Abstract
Resolution coefficient of traditional gray relation classifier usually takes a fixed value of 0.5, which greatly limits the adaptive ability, and reduces the effectiveness of this algorithm to identify signals. To solve this problem, an improved optimized resolution coefficient algorithm of gray relation classifier was proposed. Particle swarm optimization (PSO) algorithm was used to calculate the optimized resolution coefficient corresponding to the best classification results under different SNR environment. The adaptive ability of this algorithm was improved by improving the selection method of resolution coefficient and ultimately the classification effect was improved. Simulation results show that, compared with the traditional improved algorithm, it can improve the recognition rate of signals under different SNR environment, and have a good application value.
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Acknowledgment
The research of the paper is supported by the National Natural Science Foundation of China (No. 61603239), and the authors are grateful to Case Western Reserve University Bearing Data Center for kindly providing the experimental data.
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Han, H., Ying, Y., Chen, X. (2018). An Optimized Resolution Coefficient Algorithm of Gray Relation Classifier. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_37
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DOI: https://doi.org/10.1007/978-3-030-00021-9_37
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