Journal of System Simulation
Abstract
Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction model of CPSO-Elman converter gas generation. Converter gas generation is predicted on the basis of predicting the intermission in the future time. Simulation results show that prediction accuracy of the proposed method is about 5% higher than that of the method without optimization.
Recommended Citation
Fei, Jiajie; Wu, Dinghui; Fan, Junyan; and Wang, Jing
(2024)
"Prediction of Converter Gas Generation Based on Intermission Production Improved Elman,"
Journal of System Simulation: Vol. 36:
Iss.
5, Article 12.
DOI: 10.16182/j.issn1004731x.joss.23-0022
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss5/12
First Page
1179
Last Page
1188
CLC
TP391.9
Recommended Citation
Fei Jiajie, Wu Dinghui, Fan Junyan, et al. Prediction of Converter Gas Generation Based on Intermission Production Improved Elman[J]. Journal of System Simulation, 2024, 36(5): 1179-1188.
DOI
10.16182/j.issn1004731x.joss.23-0022
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