Abstract
Due to BP neural network’s own limitations, it requires a large number of samples for complex prediction, and its generalization ability is weak. This paper puts forward an optimized BP neural network algorithm. The methods focus on data normalization in order to improve maximum speed limit, the inertia constant, and fitness function to eventually optimize BP neural network weight value and threshold value to reduce its distribution range before using BP neural network for color prediction. Reducing the possibility of BP neural network prediction model getting into local optimization has good nonlinear fitting capability and higher prediction accuracy of color space conversion. If using in the ICC profile, it will ensure the accuracy of the color conversion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Y., Wu, Q., & Ma, Z. (2010). A survey of particle swarm optimization and its application. Micro Computer Information, 11(3), 72–77.
Zhou, J., Chen, J., & Liu, G. (2013). An overview of inertia weight in particle swarm optimization. Guangdong Power, 32(3), 55–56.
Grosan, C., & Abraham, A. (2007). Hybrid evolutionary algorithms, methodologies, architectures, and reviews. Studies in Computational Intelligence (SCI), 75, 1–17.
Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1, 3–31.
Eberhart, R., & Shi, Y. (2001). Particle swarm optimization: Developments, applications and resources. In Proceedings of IEEE Congress Evolutionary Computation (Vol. 1, pp. 81–86)
Parsopoulos, K., & Vrahatis, M. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1, 235–306.
Banks, A., Vincent, J., & Anyakoha, C. (2007). A review of particle swarm optimization. Part I: background and development. Natural Computing, 6, 467–484.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence, 1(1), 33–57.
van Ast, J., Babuška, R., De Schutter, B. (2008). Particle swarms in optimization and control. In Proceedings of the 17th World Congress The International Federation of Automatic Control (pp. 5131–5136). Seoul, Korea, July 6–11
Li, R-j. (2003). Study on color space conversion model of from RGB to CIEXYZ. Packaging Engineering, 30(3), 79–81.
Hong, L., & Chu, G-l. (2013). RGB to CIEXYZ color space conversion. Packaging Engineering, 34(21), 85–89.
del Valle, Y., Venayagamoorthy, G., Mohagheghi, S., et al. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12(2), 171–195.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Miao, H., Zhang, L. (2016). The Color Characteristic Model Based on Optimized BP Neural Network. In: Ouyang, Y., Xu, M., Yang, L., Ouyang, Y. (eds) Advanced Graphic Communications, Packaging Technology and Materials. Lecture Notes in Electrical Engineering, vol 369. Springer, Singapore. https://doi.org/10.1007/978-981-10-0072-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-0072-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0070-6
Online ISBN: 978-981-10-0072-0
eBook Packages: EngineeringEngineering (R0)