Optimize BpNN Using New Breeder Genetic Algorithm

Alabbas, Maytham; Jaf, Sardar; Abdullah, Abdulhussein Mohsin

doi:10.1007/978-3-319-48308-5_36

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Singha et al [10] used the initial pH value, initial Pb 2+ concentration, adsorbent dosage, and contact time as the input layer of the BP neural network to predict the removal rate of Pb 2+ in the study of hydrometallurgical extraction of lead, and the actual results showed that the prediction results of the model were excellent. Although the BP neural network is used for model prediction with high generalization ability, it needs to be optimized by an optimization algorithm due to its slow learning speed and tendency to fall into local minima [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network

et al. 2023

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The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.

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Section: Introductionmentioning

confidence: 99%