Abstract
This paper is aimed at the key technology of digital printing in the textile industry. According to the color reproduction characteristics of digital printing, a color prediction model based on Particle Swarm Optimization (PSO) was proposed to optimize the three-layer BP neural network, solving the problem that BP neural network is easy to fall into local minimum value through optimization of weights and thresholds, which effectively improved the digital printing color prediction accuracy. The experimental and industrial application results show that the prediction accuracy of this paper is higher than BP neural network model and the Yule-Nielsen modified Neugebauer model these two mainstream algorithms, which is more in line with the practical needs of digital printing industry applications.
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References
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Acknowledgements
This work is funded by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2012BAH91F03) and Hangzhou Dianzi University Graduate Innovative Research Fund (CXJJ2018017) and Digital Imaging Theory-GK188800299016-054.
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Lu, S., Wang, Q., Yang, P., Zhang, W. (2019). Research on Digital Printing Color Prediction Model Based on PSO-BP Neural Network. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ren, Y. (eds) Advances in Graphic Communication, Printing and Packaging. Lecture Notes in Electrical Engineering, vol 543. Springer, Singapore. https://doi.org/10.1007/978-981-13-3663-8_6
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DOI: https://doi.org/10.1007/978-981-13-3663-8_6
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