| 引用本文: | 张绍德,毛雪菲,毛雪芹,高尚义.基于grey Markov--支持向量机的电弧炉终点参数预报[J].控制理论与应用,2009,26(12):1443~1448.[点击复制] |
| ZHANG Shao-de,MAO Xue-fei,MAO Xue-qin,GAO Shang-yi.End-point parameter prediction for electric arc furnace based on grey Markov--support-vector-machines[J].Control Theory and Technology,2009,26(12):1443~1448.[点击复制] |
|
| 基于grey Markov--支持向量机的电弧炉终点参数预报 |
| End-point parameter prediction for electric arc furnace based on grey Markov--support-vector-machines |
| 摘要点击 1959 全文点击 847 投稿时间：2009-01-21 修订日期：2009-06-19 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/j.issn.1000-8152.2009.12.CCTA090072 |
| 2009,26(12):1443-1448 |
| 中文关键词 终点预报 灰色马尔可夫模型 支持向量机 粒子群算法 电弧炉 |
| 英文关键词 end-point prediction grey Markov model support-vector-machines particle swarm optimization electric arc furnace |
| 基金项目 安徽省科技攻关项目(01012053). |
|
| 中文摘要 |
| 考虑电弧炉终点参数既受定量因素的影响, 又受非定量因素的影响, 将灰色马尔可夫(grey Markov)与支持向量机((SVM)相结合, 建立了电弧炉终点参数grey Markov-SVM预报模型, 其中grey Markov模型反映非定量因素对电弧炉终点参数预测值的影响, SVM模型反映电弧炉各种定量输入对终点参数预测值的影响. 建立grey Markov-SVM模型的方法是: 首先建立反映非定量因素的GM(1,1)模型, 然后用Markov链修正其预测值; 由于grey Markov模型对定量输入的影响无法准确反映, 因此grey Markov模型必然存在预测偏差, 此预测偏差通过建立反映定量输入与终点参数预测偏差之间关系的SVM模型方法加以补偿, 并采用粒子群算法(PSO)对SVM的参数进行寻优, 最终得到电弧炉终点参数的预报值, 同时实现滚动预测. 仿真实验表明grey Markov-SVM模型与grey-SVM模型、Markov-SVM相比较, 具有很高的终点预报精度. |
| 英文摘要 |
| Because the end-point parameters of an electric arc furnace(EAF) are affected by both quantitative factors and non-quantitative factors, we combine the grey Markov model with support-vector-machines(SVM) to produce a grey Markov--SVM prediction model for estimating the end-point parameter values of an EAF. The effects from the nonquantitative factors on the prediction values of end-point parameters are reflected by the grey Markov model; while the effects from the quantitative inputs are reflected by the SVM. The GM(1,1) model that reflects non-quantitative factors is established firstly, and then, its prediction values are revised by the Markov chain. Because the effect from the quantitative inputs can not be reflected by the greyMarkov model, the grey Markov-model is certainly not free from prediction errors from the quantitative inputs. These prediction errors are compensated by the SVM model with parameters optimized by particle swarm optimization(PSO) algorithm. The final prediction values of the end-point parameters in EAF are thus obtained. Meanwhile, the rolling forecasting is realized. Experiments show that the grey Markov--SVM model has the best prediction precision in comparison with the grey SVM model or the Markov--SVM model. |
|
|
|
|
|