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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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