Target prediction in blast furnace based on BP network optimized by variable neighborhood PSO
Vernadsky National Library of Ukraine
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Target prediction in blast furnace based on BP network optimized by variable neighborhood PSO
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| Creator |
Yang Kai
Zhijun He |
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| Subject |
Modeling and simulation
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| Description |
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.
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| Date |
2017-06-14T09:43:36Z
2017-06-14T09:43:36Z 2016 |
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| Type |
Article
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| Identifier |
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.
1027-5495 DOI: dx.doi.org/10.15407/fm23.03.463 http://dspace.nbuv.gov.ua/handle/123456789/121413 |
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| Language |
en
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| Relation |
Functional Materials
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| Publisher |
НТК «Інститут монокристалів» НАН України
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