Funct. Mater. 2016; 23 (3): 463-467.

Target prediction in blast furnace based on BP network optimized by variable neighborhood PSO

Yang Kai1,2, Zhijun He1

1School of materials and metallurgy, University of Science and Technology LiaoNing, LiaoNing 114051, China
2 School of Software, University of Science and Technology LiaoNing, LiaoNing 114051, China


This paper combine the improved PSO algorithm (Analysis of Particle Swarm Optimization Algorithm) with the BP neural network for prediction of Silicon content in hot metal. Firstly, the varying visual mechanism is drawing into the standard PSO through changing the neighbor structure dynamically with each particles, in order to enhance the local and global searching ability in particle swarm. Afterwards, the improved algorithm is used to optimize the weights and threshold of BP neural network to avoid falling into local extremum. Finally, the prediction model of Si content in hot metal is built based on BP network optimized by Variable neighborhood PSO. The average relative error of the prediction model is 6.7% based on the data from blast furnace.

particle swarm optimization, neural network, silicon content, prediction.

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