一种基于加权LS-SVM的复合辨识方法  

A Compound Identification Method Based on Weighted Least Square Support Vector Machine

作　　者：马丛俊 王海鹏 张旭 赵涛[1] 向国菲[1] 佃松宜[1] MA Congjun;WANG Haipeng;ZHANG Xu;ZHAO Tao;XIANG Guofei;DIAN Songyi(College of Electrical Engineering Sichuan University,Chengdu 610065 China;State Grid Intelligent Technology Co.Ltd,Ji’nan 250101 China)

机构地区：[1]四川大学电气工程学院,成都610065 [2]国网智能科技股份有限公司,济南250101

出　　处：《电光与控制》2021年第5期42-45,102,共5页Electronics Optics & Control

基　　金：国家电网公司总部科技项目(面向GIS设备腔体内维护机器人关键技术研究项目)。

摘　　要：针对采用含随机噪声的数据进行非线性动态系统建模无法获得准确模型参数的问题,提出了一种基于加权最小二乘支持向量机(LS-SVM)数据预处理的复合辨识方案。复合辨识方案根据数据的分布信息,利用加权LS-SVM对异常数据的鲁棒性,通过回归计算消除数据的异常性;再利用预处理后的数据训练补偿模糊神经网络参数,得到系统模型。结果表明,采用加权LS-SVM预处理的复合辨识方案运用在普遍存在随机噪声的系统中,能获得更加准确的辨识模型。Aiming at the problem that the accurate model parameters are not available for establishing a nonlinear dynamic system model to the data with random noise a composite identification scheme with data preprocessing based on weighted Least Square-Support Vector Machine(LS-SVM)is proposed.According to the distribution information of the data and by use of the robustness of weighted LS-SVM to abnormal data the identification scheme removes anomalism of the data through regression calculation.Further more the preprocessed data is used for data training to compensate for parameters of the fuzzy neural network and the system model is obtained.The proposed composite identification scheme behaves better in the simulation of system identification with random noise.

关 键 词：数据预处理 加权LS-SVM 补偿模糊神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]