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论文摘要

模糊系统结合蚁群算法的金具温升建模

Modeling the Rise of Temperature of Fitting by Combining Fuzzy System and Ant Colony Algorithm

作者:王刚(平高集团有限公司);谭盛武(平高集团有限公司);林生军(平高集团有限公司);何子博(西安工业大学 材料与化工学院);常林晶(平高集团有限公司);杨国华(平高集团有限公司)

Author:Wang Gang(Pinggao Group Co.,Ltd.);Tan Shengwu(Pinggao Group Co.,Ltd.);Lin Shengjun(Pinggao Group Co.,Ltd.);He Zibo(School of Materials and Chemical Eng. of Xi’an Technological Univ.);Chang Linjing(Pinggao Group Co.,Ltd.);Yang Guohua(Pinggao Group Co.,Ltd.)

收稿日期:2015-07-27          年卷(期)页码:2016,48(5):167-172

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:阀厅连接金具;模糊系统;基本蚁群算法;梯度下降法;改进蚁群算法

Key words:connection fitting in value hall;fuzzy system;basic ant colony algorithm;gradient descent algorithm;improved ant colony algorithm

基金项目:国家高技术研究发展计划资助项目(2014AA051802) ;国家电网科技资助项目(SGNXJX00YJJS1400105)

中文摘要

为了得到准确可靠的阀厅连接金具温升模型,运用模糊系统结合蚁群算法的方法进行建模。在分析基本蚁群算法与梯度下降法优缺点的基础上,将两种方法结合形成改进蚁群算法,即在基本蚁群算法基础上应用梯度下降算法。通过试验得到的训练数据分别用基本蚁群算法、梯度下降算法、改进蚁群算法训练模糊系统,改进蚁群算法的收敛效果优于其他两种方法;通过试验得到的测试数据对4种方法所得的模型进行测试,由改进蚁群算法训练模糊系统所得模型的测试效果是最好的。结果表明,若能通过试验得到足量训练数据,用改进蚁群算法训练模糊系统的方法对阀厅连接金具的温升进行建模是可行的。

英文摘要

In order to get an accurate and reliable model of the rise of temperature of connection fitting in value hall of UHVDC,a method of fuzzy system combined ant colony algorithm was used.After analyzing the characteristics of basic ant colony algorithm and gradient descent algorithm,an improved ant colony algorithm was put forward by combining basic ant colony algorithm and gradient descent algorithm,in which gradient descent algorithm was processed after basic ant colony algorithm. Through training data obtained from experiment,the fuzzy system was trained by basic ant colony algorithm,gradient descent algorithm and improved ant colony algorithm respectively.The convergence effect of improved ant colony algorithm was better than that of other two algorithms.All models were tested by testing data obtained from experiment,and the prediction effect of the fuzzy system trained by improved ant colony algorithm was best of all models.The prediction results showed that if training data obtained from experiment was enough,the fuzzy system trained by improved ant colony algorithm was reliable to predict the rise of temperature of connection fitting.

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