Composition analysis and identification model of ancient glass products based on Spearman correlation coefficient and BP neural network

Xiang Yang; Wencheng Wang; Yanxia Zhou

doi:10.54097/hset.v34i.5486

Authors

Xiang Yang
Wencheng Wang
Yanxia Zhou

DOI:

https://doi.org/10.54097/hset.v34i.5486

Keywords:

Spearman correlation coefficient; BP neural net; Cluster analysis; Heat map.

Abstract

This paper analyzes the composition of glass products by statistical analysis of known data and then uses Spearman's correlation coefficient analysis model to analyze the composition of glass products, and uses BP neural network to build a prediction and classification model. We use BP neural network to build a prediction and classification model for the composition of glass products and classify the types. In this paper, a BP neural network classification model is constructed using C language. The number of neuron nodes in the input layer and the number of neuron nodes in the output layer are selected. The chemical composition content of the existing sample data is used as the input layer and the category is converted to the output layer after data pre-processing. Finally, a BP neural network classification model with very low classification error is obtained. The sensitivity of the classification results is obtained by sample carryover network calibration and Spearman correlation coefficient.

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