Developing the network social media in graphic design based on artificial neural network

Yaxuan Liu ()
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Yaxuan Liu: Luxun Academy of Fine Arts

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 4, No 3, 640-653

Abstract: Abstract The purposes are to effectively solve the graphic design problems, develop an easy-to-use supporting design program, and make graphic design more reliable and accurate. Based on the analysis of the current graphic design framework, graphic design data are obtained from the network social media. A system of surrounding rock classification and support optimization design is developed by a deep neural network structure. The model’s effectiveness is verified by more than 3000 road conditions data. The results show that the three-layer network’s errors are 0.0062 with a training time of 12,455, and the five-layer network’s errors are 0.00019 with a training time of 69,895. With the input layer, hidden layer, and the output layer of 8, 15, and 5 respectively, the model performs best. In the deep learning algorithm, the deep backpropagation neural network (Deep BPNN) can obtain the best training effects with less training time. Therefore, the roadway drawing system’s application based on the deep learning algorithm to the roadway support design can improve design efficiency and scientificity.

Keywords: Deep learning algorithm; Weight coefficient; Bias matrix; Accuracy requirement; Roadway support design (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13198-021-01058-2

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