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首页> 外文期刊>Neural computing & applications >Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer
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Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

机译：使用改进的粒子群优化器优化的支持向量机对高炉中铁水硅含量进行建模

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As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.
机译：高炉作为高度复杂的多输入多输出系统，在工业发展中发挥着重要作用。尽管在过去的几十年中进行了大量研究，但仍然存在许多要解决的问题，例如建模问题。本文采用支持向量回归（SVR）建立高炉硅含量的预测模型。为了确保给定数据集的良好泛化性能，为SVR选择适当的参数很重要。针对这一问题，提出了一种新的粒子群优化器DMS-PSO-CLS，以优化SVR的参数。在DMS-PSO-CLS中，一种新的合作学习策略与DMS-PSO混合在一起，这使得粒子信息可以更有效地用于生成更好质量的解决方案。 DMS-PSO-CLS结合了DMS-PSO和协作学习策略的优点，可以提高收敛速度和收敛精度。实验结果表明，DMS-PSO-CLS可以快速找到SVR的最佳参数，而DMS-PSO-CLS优化的SVR模型可以对高炉的预测问题实现良好的回归精度。

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