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

• Published in: Functional Materials
• Date: 2016
• Main Authors: Yang Kai, Zhijun He
• Format: Article
• Language: English
• Published: НТК «Інститут монокристалів» НАН України 2016
• Subjects: Modeling and simulation
• Online Access: https://nasplib.isofts.kiev.ua/handle/123456789/121413
• Journal Title: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Cite this: Target prediction in blast furnace based on BP network optimized by variable neighborhood PSO / Yang Kai, Zhijun He // Functional Materials. — 2016. — Т. 23, № 3. — С. 463-467. — Бібліогр.: 8 назв. — англ.

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.