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  4. Vol 4, Issue 2, 2023

Classification of Ancient Glass Based on Perceptron Model

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DOI: 10.23977/autml.2023.040203 | Downloads: 18 | Views: 472

Author(s)

Cui Wang 1

Affiliation(s)

1 School of Management, Shandong University of Technology, Zibo, Shandong, 255000, China

Corresponding Author

Cui Wang

ABSTRACT

Weathering process will make a lot of elements inside the glass and environment elements exchange, resulting in the change of the composition ratio. In order to classify and subclassify ancient glass products, this paper uses the perceptron model improved based on particle swarm optimization to solve the binary classification problem of high-potassium glass and lead-barium glass. With the accuracy of prediction as the objective function, the 14-dimensional weight vector is obtained by learning all the data sets that have been converted to un-weathered. In the further subclass division, cluster analysis was used in this paper to subdivide the two kinds of glass, high potassium and lead barium, respectively, and finally they were divided into three categories.

KEYWORDS

Particle swarm optimization, Perceptron model, Classification of ancient glass

CITE THIS PAPER

Cui Wang, Classification of Ancient Glass Based on Perceptron Model . Automation and Machine Learning (2023) Vol. 4: 16-23. DOI: http://dx.doi.org/10.23977/autml.2023.040203.

REFERENCES

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[5] Sean P. Rigby, Lee Stevens, Thomas Meersmann, Galina E. Pavlovskaya, Gregory J. Rees, Julian Henderson, Saffron J. Bryant, Karen J. Edler, Robin S. Fletcher. Structural and chemical heterogeneity in ancient glass probed using gas overcondensation, X-ray tomography, and solid-state NMR [J]. Materials Characterization, 2020, 167. 
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[8] Yang Wei, Li Qiqiang. Overview of Particle Swarm Optimization Algorithms [J]. Engineering Science, China, 2004(05): 87-94.

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