基于IPSO-SVR的水泥分解炉温度预测模型研究
发布时间：2018-10-20 09:20
【摘要】：为建立稳定可靠的分解炉温度预测模型,结合与分解炉温度密切相关的几个主要运行参数,提出一种粒子群参数优化的支持向量回归机算法(PSO-SVR),并在粒子群算法中引入自适应惯性权重的思想,构建出分解炉温度预测模型。与未改进的模型进行仿真对比实验,实验结果表明,该IPSO-SVR模型具有较佳的预测能力,预测相关系数达到0.707 5,温度预测误差绝对值不超过7℃,误差率在0.8%以内。
[Abstract]:In order to establish a stable and reliable calciner temperature prediction model, combined with several main operating parameters closely related to calciner temperature, A support vector regression algorithm (PSO-SVR) for particle swarm optimization (PSO) is proposed, and the adaptive inertial weight is introduced into PSO to construct the temperature prediction model of calciner. Compared with the unimproved model, the experimental results show that the IPSO-SVR model has better prediction ability, the prediction correlation coefficient is 0.707, the absolute value of temperature prediction error is less than 7 鈩，
本文编号：2282711
[Abstract]:In order to establish a stable and reliable calciner temperature prediction model, combined with several main operating parameters closely related to calciner temperature, A support vector regression algorithm (PSO-SVR) for particle swarm optimization (PSO) is proposed, and the adaptive inertial weight is introduced into PSO to construct the temperature prediction model of calciner. Compared with the unimproved model, the experimental results show that the IPSO-SVR model has better prediction ability, the prediction correlation coefficient is 0.707, the absolute value of temperature prediction error is less than 7 鈩，
本文编号：2282711
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