International Journal of Bio-Inspired Computation· 2023Q2
Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm
- 65citations
- Q2SCImago
- 2023year
Short summary
A new method, DGOLSCPP, uses a densely connected GRU (DGRU) and an optimized salp swarm algorithm (OLSSA) to predict coke prices more accurately.
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Key points
- Proposes DGOLSCPP, combining dense GRU (DGRU) for feature extraction and an optimized salp swarm algorithm (OLSSA) for parameter tuning.
- DGRU model uses two stacked layers to capture deeper time-series features.
- OLSSA incorporates opposition-based learning and stochastic walk to improve search capabilities.
- Experimental results on real-world coke price datasets show superior performance compared to other methods.
AI-generated from the title and abstract; the full text is not read.
Abstract
Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.
The authors' abstract, as published at the source. International Journal of Bio-Inspired Computation, 2023 · DOI ↗
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Field: Fuel Technology
Fuel TechnologyEnergy