【作者】 梅振益；

【导师】 杨慧中；

【作者基本信息】 江南大学 ， 控制理论与控制工程， 2011， 硕士

【摘要】 工业生产过程是一个复杂的过程,存在着多工况、非线性、高噪声等特点。在对其生产过程中的难测变量软测量建模时,如果采用单模型一般难以描述整个工况特性,并对噪声的处理能力比较弱。因此采用多模型软测量的方法能比单模型更好的描述生产过程,使得软测量模型的精度得到提高,鲁棒性得到加强。由于多模型软测量方法有这些优点,本文开展了如下四种不同多模型方法的研究,并将多模型软测量方法应用到一个实际的化工生产装置中。具体的研究成果如下:1、提出了一种基于加权模糊聚类方法的多模型建模方法。将输入向量与输出的相关性作为加权系数,构建加权模糊聚类算法,对样本空间的输入数据进行聚类,然后分别建立各子模型。测试时采用开关切换方式将输入变量送入对应的子模型进行输出估计,子模型输出作为系统模型的最终输出。该方法能够实现对输入数据更加合理的划分,软测量模型的精度得到了提高。将该方法应用于裂解反应器出口双酚A组分的软测量建模中,仿真结果表明,该方法是一种可行的、有效的软测量建模方法。2、提出了一种基于疏密部数据划分的软测量多模型建模方法。该方法充分应用了全局核函数和局部核函数的特性,以最近邻聚类法为基础,将输入样本数据分为疏部与多个密部,对疏部采用全局核函数,对密部采用局部核函数,构建加强型支持向量分类机子模型,得到由多模型组成的软测量模型。通过对苯酚蒸发器出口双酚A组分的仿真研究表明,该模型的泛化能力得到了提高。3、提出了一种基于混沌差分进化模糊C-均值聚类的多模型建模方法。该方法采用混沌差分进化算法对模糊C-均值聚类的目标函数进行全局寻优,能有效的解决模糊C-均值聚类陷入局部最优的问题。将该方法应用于重排反应器出口双酚A组分的软测量建模中,仿真结果表明了该算法构造的软测量多模型的有效性。4、提出了一种基于改进的局部保持投影算法的多模型建模方法。该方法通过有监督的自适应权值的局部保持投影算法对输入数据空间进行特征提取,并结合最近邻分类器算法进行输入空间的划分,最后融合支持向量机实现多模型建模。将该方法应用于苯酚蒸发器出口双酚A组分的软测量建模中,仿真结果表明,该方法的分类精度高于传统的局部保持投影和最近邻分类器结合算法,该方法的模型估计精度得到了提高,具有更强的泛化能力。更多还原

【Abstract】 Industrial production is a complex process with characters of multiple-conditions, nonlinear, high noise, etc. When soft approach is used for unpredictable variables, normally single model can’t effectively describe the characteristics of working conditions and is less able to deal with noise. Contrary with single model, multiple models can describe complex production process better and improve the estimated accuracy and generalization ability. According to the engineering application background, four methods for multi-model modeling are proposed in this thesis. Specific results are as follows:1. A multi-model modeling method based on weighted fuzzy clustering is presented. By using the correlation of input and output as weighted coefficient of fuzzy clustering, it is employed to cluster the input data of sample space, and respectively build sub-models. And then using switch mode, the corresponding sub-models estimate output and final output is determined by the corresponding sub-model output. This method can achieve a more rational division of the input data to improve the accuracy of soft-sensor model. The multi-model is applied to a soft sensor for components of BPA in a cracking reactor exports, and the simulation results show its feasibility and effectiveness.2. A novel method of multi-modeling for soft sensor is proposed in the paper. The method divides the input sample set into one sparse part and several dense parts based on the nearest neighbor clustering algorithm which it fully apply the characteristics of global and local kernel function. Meanwhile, global kernel and local kernel are applied to construct corresponding sub-model with Enhanced Support Vector Classifier. Finally, a soft sensor system with multi-model is obtained. Using the proposed algorithm to the soft-sensor model of BPA component in a Phenol evaporator outlet, the result of simulation shows the effectiveness of the algorithm.3. A novel fuzzy C-Mean clustering based on Chaotic Differential Evolution is presented, which is for multiple models soft-sensing modeling. The proposed algorithm optimizes objection function of fuzzy C-Mean clustering by using Chaotic Differential Evolution and gets a global optimal solution, which can effectively address the problems of local optimum for fuzzy C-Mean clustering. The multi-model is applied to estimating the components of BPA in a Rearrange reactor exports, it is shown that the algorithm is effective.4. A Multi-model soft senor based on Improved Locality Preserving Projection is proposed. The proposed approach extracts the features of input sample space by Supervised and Adapt Weighted Locality Preserving Projection. The multi-models can be constructed by Support Vector Machine after using the nearest neighbor classifier to divide input data space. Using the proposed algorithm to the soft-sensor model of BPA component, the result of simulation shows that the proposed approach has better performance compared with conventional Locality Preserving Projection’s, and has superior in accurate estimation, and generalization ability.更多还原