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Prediction of component content in rare earth extraction process based on ESNs-Adaboost

机译：基于ESNS-Adaboost的稀土提取过程中成分含量的预测

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摘要

Aiming at the problem that the component content of elements in REEP (rare earth extraction process) is difficult to detect, a modeling method for component content in REEP based on ESNs-Adaboost is proposed. It uses ESN (echo state network) with fast training speed and high stability to establish multiple identification model of REEP. And then combined with the improved Adaboost algorithm, multiple models are integrated into a final ESNs-Adaboost model of component content of REEP according to the combination rule. The improved Adaboost algorithm has an advantage that its threshold can be adjusted adaptively with the training error. By collecting data from CePr/Nd extraction process, the simulation results show that the proposed method has high prediction accuracy, strong generalization ability, good robustness and better performance than single ESN model, which can meet the requirements of rapid detection of component content in extraction field.
机译：旨在解决REEP中元素的组分含量（稀土提取过程）难以检测的问题，提出了基于ESNS-Adaboost的REEP中组分含量的建模方法。它使用ESN（回声状态网络）具有快速训练速度和高稳定性来建立REEP的多个识别模型。然后与改进的Adaboost算法结合，根据组合规则将多个模型集成到REEP的组件内容的最终ESNS-Adaboost模型中。改进的Adaboost算法具有优点，即可以通过训练误差自适应地调整其阈值。通过从CEPR / ND提取过程中收集数据，仿真结果表明，该方法具有高预测精度，强大的泛化能力，良好的鲁棒性和比单一ESN模型的更好性能，这可以满足提取中快速检测组分含量的要求场地。

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