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).
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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
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DOI: https://doi.org/10.1007/978-3-540-87732-5_20
Publisher Name: Springer, Berlin, Heidelberg
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