| 引用本文: | 唐苦,王昕,王振雷.基于证据合成规则的多模型软测量[J].控制理论与应用,2014,31(5):632~637.[点击复制] |
| TANG Ku,WANG Xin,WANG Zhen-lei.Multi-model soft sensor based on Dempster-Shafer rule[J].Control Theory and Technology,2014,31(5):632~637.[点击复制] |
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| 基于证据合成规则的多模型软测量 |
| Multi-model soft sensor based on Dempster-Shafer rule |
| 摘要点击 2261 全文点击 1408 投稿时间：2013-07-05 修订日期：2014-01-08 |
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| DOI编号 10.7641/CTA.2014.30678 |
| 2014,31(5):632-637 |
| 中文关键词 证据合成规则 多模型 数据融合 仿射传播聚类 软测量 |
| 英文关键词 Dempster-Shafer rule multi-model data fuse affinity propagation cluster soft sensor |
| 基金项目 国家重点基础研究发展计划资助项目(2012CB720500); 国家自然科学基金资助项目(U1162202);国家863计划资助项目(2013AA0407 01); 十二五国家科技支撑计划资助项目(2012BAF05B00); 上海市科技攻关资助项目(12dz1125100); 上海市重点学科建设资助项目 (B504); 流程工业综合自动化国家重点实验室开放课题基金. |
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| 中文摘要 |
| 针对传统软测量方法存在的预测性能差、融合能力低和适应性不强等缺点, 本文提出了一种基于证 据(D–S)合成规则的多模型软测量方法. 首先, 利用仿射传播(AP)聚类方法和最小二乘支持向量机(LS–SVM)建立多 个子模型; 然后, 利用D–S合成规则得到多个证据概率分配函数, 将其作为权值因子对子模型输出进行融合得到多 模型的输出, 提高了模型的预测能力和融合能力; 最后, 将上述方法用于非线性系统和酯化率的软测量建模, 仿真结 果表明, 相比于单一模型和传统的多模型软测量方法, 本文方法具有更好的预测性能和精度, 是一种有效的软测量 方法. |
| 英文摘要 |
| There are disadvantages in traditional model methods for the soft sensor, such as low predictive accuracy, poor fusion ability and weak adaptability. In this paper, a multi-model soft sensor method is proposed based on Dempster- Shafer (D–S) rule. Firstly, the affinity propagation (AP) clustering method and the least squares support vector machine (LS–SVM) are used to establish multiple sub-models. Then, the multi-model output of the soft sensor is obtained through the fusion of the sub-models based on the weighting factor calculated by using D–S rules to improve the model prediction ability and fusion ability. The proposed method is used to build the soft sensor model of a nonlinear system and the ester rate. Simulation results and industry application indicate that the proposed method has better predictive performance and higher accuracy in comparison with the traditional soft sensor. |
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