The Spectral Characterizing Model Based on Optimized RBF Neural Network for Digital Textile Printing

  • Zhihong Liu
  • Yongjun Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 477)


Digital textile printing was appeared in 1980s, which printed directly onto the surface of fabric with digital original by ink-jet printer and got high quality color printing textile. The spectral characterization of printer is the key technology for the textile printing. This paper presented a spectral characterizing model based on RBF neural network, which optimized the RBF neural network by extending the input variables of neural network. Experimental results showed that the 90% spectral errors of testing color samples are less than 0.04, the average spectral error is 0.025, the maximum spectral error is 0.066; while the 90% color differences (ΔE2000) of testing color samples are less than 2.8, the average value is 1.89, the maximum value is 8.5. That means the model could effectively improve the characterization chromaticity and spectral precision.


Spectral characterization Spectral model Optimized neural network Digital textile printing 


  1. 1.
    Ujiie H (2006) Digital printing of textiles. Woodhead Publishing, Boca Raton, pp 201–288CrossRefGoogle Scholar
  2. 2.
    Zhou R, Yu Z (2010) Unscrambling the development status-quo of international nonwoven equipment. China Text Leader 9:70–72Google Scholar
  3. 3.
    Aehwal WB (2002) Textile chemical principles of digital textile printing (DTP). Colour ge 12:33–34Google Scholar
  4. 4.
    Tyler DJ (2005) Textile digital printing technologies. Text Prog 37(4):1–65CrossRefGoogle Scholar
  5. 5.
    Calvert P (2001) Inkjet printing for materials and devices. Chem Mater 13(10):3299–3305CrossRefGoogle Scholar
  6. 6.
    Mikuz M, Turk SS, Tavcer PF (2010) Properties of ink-jet printed ultraviolet-cured pigment prints in comparison with screen-printed, thermos-cured pigment prints. Color Technol 126(5):249–255CrossRefGoogle Scholar
  7. 7.
    Daplyn S, Lin L (2003) Evaluation of pigmented ink formulations for jet printing onto textile fabrics. Pogment Resin Technol 32(5):307–318CrossRefGoogle Scholar
  8. 8.
    Huang Y, Cao B, Xu C et al (2015) Synthesis process control and property evaluation of a low-viscosity urethane acrylate oligomer for blue light curable ink of textile digital printing. Text Res J 8597:759–767CrossRefGoogle Scholar
  9. 9.
    Wan XX, Liu Q (2014) Review of spectral printer characterization. J Image Graph 19(7):985–997Google Scholar
  10. 10.
    Sarimveis H, Doagnis P, Alexandridis A (2006) A classification technique based on radial basis function neural networks. Adv Eng Softw 37(4):218–221CrossRefGoogle Scholar
  11. 11.
    da Cruz LF, Freire RZ, Reynoso-Meza G et al (2016) RBF neural network combined with self-adaptive mode and genetic algorithm to identify velocity profile of swimmers. In: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1 – 7Google Scholar
  12. 12.
    Rutemiller H, Bowers D (1968) Estimation in a heteroscedastic regression model. J Am Stat Assoc 63:552–557MathSciNetGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhihong Liu
    • 1
  • Yongjun Liang
    • 2
  1. 1.School of Media & CommunicationShenzhen PolytechnicShenzhenChina
  2. 2.Group Technology CenterShenzhen Yuto Packaging Technology Co., LtdShenzhenChina

Personalised recommendations