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作 者:马俊林 曹梦龙[1] MA Junlin;CAO Menglong(School of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
机构地区:[1]青岛科技大学自动化与电子工程学院,山东青岛266061
出 处:《自动化仪表》2022年第9期18-22,29,共6页Process Automation Instrumentation
基 金:山东省自然科学基金资助项目(ZR2020KE037)。
摘 要:塑料裂解工艺的发展与研究对于保护环境和节约资源具有深远意义。传统塑料裂解温控系统存在工况复杂、无法建立准确模型、非线性强耦合的问题。以提高传统模糊控制提取规则库的完备性与准确性为目的,对一种新型样本相关性模糊规则提取相关性因子改进Wang-Mendal(CWM)算法进行研究,即从样本数据中通过计算样本相关性因子来提取可靠的模糊规则。首先,在传统模糊规则提取Wang-Mendal(WM)算法基础之上结合基于密度峰值的快速聚类算法与减法聚类算法对样本数据进行降噪预处理。然后,根据输出值及其对应的样本相关性因子计算每个模糊规则的平均输出值,并由此完善模糊规则库。最后,将改进算法与其他传统算法进行对比。仿真结果表明,该改进模糊规则提取算法可提高模糊规则以及裂解温控过程的可靠性和准确性。The development and research of plastic cracking process has far-reaching significance for protecting the environment and saving resources.The traditional plastic cracking temperature control system has the problems of complex working conditions,inability to establish accurate models,and strong nonlinear coupling.With the aim of improving the completeness and accuracy of the rule base extracted by traditional fuzzy control,a novel sample-relative fuzzy rule extraction correlation factor Wang-Mendal(CWM)algorithm is investigated,i.e.,reliable fuzzy rules are extracted from sample data by calculating sample-relative factors.Firstly,the sample data is pre-processed with noise reduction based on the traditional fuzzy rule extraction Wang-Mendal(WM)algorithm combined with the fast-clustering algorithm based on density peaks and the subtractive clustering algorithm.Then,the average output value of each fuzzy rule is calculated based on the output value and its corresponding sample relevance factor,and the fuzzy rule library is thus improved.Finally,the improved algorithm is compared with other traditional algorithms.The simulation results show that the improved fuzzy rule extraction algorithm can improve the reliability and accuracy of fuzzy rules as well as cracking temperature control process.
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