Abstract
This paper utilizes data fusion algorithm to analyze the multi-sensors data, in order to obtain accurate and complete information in the sugar boiling process, and to improve the accuracy and reliability of decision-making. Proposing an improved particle swarm optimization method(BWPSO), compared with the basic particle swarm optimization, each particle adjust its position not only according to its own previous best solution and its group’s previous best, but also its group’s previous worst. This paper integrates BP neural network and particle swarm optimization, utilizes BWPSO algorithm to optimize BP neural network’s connection weights, and exerts BWPSO’s capability in global optimization and BP’s advantage in local search sufficiently. The experimental results show that utilizing BWPSO to optimize BP can get faster convergence, higher precision and better effect.
The research is supported by Guangxi Province Nanning Key Technology Research Project (200501007A).
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, W.J.: Sugar Cane Principle and Technology (volumes 4) [M]. China Light Industry Press, Beijing (2001)
He, Y.: Multi-sensor Data Fusion and Applications [M]. Electronic Industry Press, Beijing (2000)
Zhao, H., Liu, L.Y., Zhang, G.X.: Neural Network Training Based on Compound Fitness Particle Swarm Optimization [J]. Control and Decision 20(8), 958–960 (2005)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization[C]. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory [C]. In: Proceedings of the Sixth International Symposium an Micro Machine and Human Science, Nagoya, Japan, pp. 34–43 (1995)
Wang, J.N., Shen, Q.T., Chen, X.Z.: Evolutionary Design of Adaptive Neuro-Fuzzy Inference System Based on Hybrid Cooperative Particle Swarm Optimization. Systems [J] Engineering-theory & Practice 8, 48–54 (2006)
Zeng, J.C., Jie, J., Cui, Z.H.: Particle Swarm Optimization [M]. Science press, Beijing (2004)
Sheng, H.H.: Reactive Power Optimization Based on Adaptive Mutation Multi-group Particle Swarm Optimization Algorithm [D]. North China Electric Power University, Beijing (2006)
Li, C.H., Ji, C.M., Li, W.W.: Modified Particle Swarm Algorithm and Its Application in Reservoir Operation Optimization[J]. China Rural Water and Hydropower 2, 54–56 (2006)
Yang, C.M., Simon, D.: A New Particle Swarm Optimization Technique [C]. In: Proceedings of the 18th international Conference on Systems Engineering (ISCEng 2005), IEEE Computer Society, Washington (2005)
Li, F.F., Zhao, Y.K., Jia, Y.Y.: Application of Neural Network Based on Particle Swarm Algorithm for the Prediction of Oil Quality [J]. Journal of Computer Applications 26(5), 1122–1124 (2006)
Sun, J.: Sensor Nonlinear Error Compensation Evolved by Neural Network and Particle Swarm Algorithm [J]. Electronic Components & Materials 24(12), 17–19 (2005)
Chen, G.C., Yu, J.S.: Enhanced Particle Swarm Optimization and Its Application in Soft-sensor [J]. Control and Decision 20(4), 377–381 (2005)
Zhi-hong, Y., Chun-guang, W., Jun-qing, F.: SAPSO Neural Network for Inspection of Non-development Hatching Eggs[C]. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 85–88. Springer, Heidelberg (2006)
Gao, Y.X., Li, Y.S.: Image Recognition by BP Neural Networks Model Based on Particle Swarm Optimization [J]. Journal of Wuhan Polytechnic University 25(4), 35–38 (2006)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization [A]. In: Proceedings of the 1998 International Conference on Evolutionary Computation [C], pp. 84–89. IEEE, New York (1998)
Wen, S.C.: Research on Neural Networks and Its Application in Sugar Boiling [D]. South China University of Technology, Guangzhou (2002)
Wei, H.K.: Neural Network Architecture Design Theories and Methods [M]. National Defense Industry Press, Beijing (2005)
Demuth, H., Beale, M.: Neural Network Toolbox User’s Guide-Neural Network Toolbox for Use with MATLAB. The MathWorks, Inc. (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, Y., Yan, S., Tang, Z., Chen, Y., Liu, J. (2008). Data Fusion Based on Neural Networks and Particle Swarm Algorithm and Its Application in Sugar Boiling. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)