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Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm.

Wang JS, Han S - Comput Intell Neurosci (2015)

Bottom Line: Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum.Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model.Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114044, China.

ABSTRACT
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

No MeSH data available.


Structure of the proposed soft-sensor model of flotation process.
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Related In: Results  -  Collection


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fig2: Structure of the proposed soft-sensor model of flotation process.

Mentions: The structure of the proposed feed-forward neural network (FNN) soft-sensor model of the flotation process based on PSO-GSA algorithm is shown in Figure 2.


Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm.

Wang JS, Han S - Comput Intell Neurosci (2015)

Structure of the proposed soft-sensor model of flotation process.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4637155&req=5

fig2: Structure of the proposed soft-sensor model of flotation process.
Mentions: The structure of the proposed feed-forward neural network (FNN) soft-sensor model of the flotation process based on PSO-GSA algorithm is shown in Figure 2.

Bottom Line: Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum.Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model.Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114044, China.

ABSTRACT
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

No MeSH data available.