基于PSO-FCM优化T-S模型的PVC汽提塔塔顶温度系统辨识

Gao Shu-zhi, D. Xing, Gao Xianwen
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引用次数: 3

摘要

鉴于T-S模型易于表达复杂动态系统的特点和粒子群算法易于找到复杂问题的最优解的特点。本文提出了一种基于T-S模型的FCM参数优化方法。利用该方法建立了PVC汽提塔塔顶温度系统的数学模型。首先,利用c均值聚类模糊(FCM)算法的自适应聚类数,找到合适的聚类数,确定模糊规则数和模型的前提参数;利用粒子群算法对FCM算法进行优化,最后利用基于粒子群算法的FCM算法得到最佳隶属矩阵。然后,应用最小二乘算法确定T-S模型后段的参数。仿真结果表明了该建模方法的有效性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The temperature system identification of the PVC stripper tower top based on PSO-FCM optimized T-S model
In view of the characteristics of T-S model, such as easily expressing complex dynamic systems and the characteristics of PSO algorithm which could find the optimal solution of complex problems easily. This paper will presents a new identification method based on the T-S model in which FCM parameters is optimized by PSO. The mathematical model of the temperature system of the PVC stripper tower top will be built by this method. First, an adaptive number of clusters of C-means clustering fuzzy (FCM) algorithm is used to find the appropriate number of clusters in FCM, and both the number of fuzzy rules and the premise parameters of the model can are determined. Using PSO algorithm to optimize the FCM algorithm, then getting the best membership matrix by the FCM algorithm based on PSO in the end. Then, a least square algorithm is applied to determine the parameters of consequent part of T-S model. The simulation result shows the effectiveness and feasibility of the modeling method.
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