Friction Modelling Based on Support Vector Regression Machines and Genetic Algorithms

被引：0
|
作者：
Zhou, Jin-zhu [1 ]
Huang, Jin [1 ]
Zhou, Jing [1 ]
Li, Hua-ping [1 ]
机构：
[1] Xidian Univ, Minist Edu, Key Lab Elect Equipment Struct, Xian, Shaanxi Prov, Peoples R China
来源：
2008 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, VOLS 1-3 | 2008年
关键词：
Friction Modelling; Genetic Algorithms; Support Vector Regression Machines; Optimization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An accurate friction model is necessary for friction compensation in radar servo systems or industrial robots. In order to obtain an accurate friction model, a method of friction modelling is proposed, based on support vector regression machines (SVRM) and real genetic algorithms (RGA). Three optimization problem formulations are proposed to realize the automatic optimal parameter selection of SVMR to avoid spending much time on parameter selection. Moreover, a friction modelling tool using the proposed method is developed. Some comparisons are carried out on the three formulations of the proposed parameter selection. The comparison results demonstrate that the third formulations can obtain better friction model by using RBF kernel function.
引用
收藏
页码：1076 / 1081
页数：6
相关论文
共 50 条
  • [41] Applying genetic algorithms and support vector machines to the gene selection problem
    de Souza, Bruno Feres
    Carvalho, Andre C. P. L. F.
    Ticona, Waldo Cancino
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2007, 18 (05) : 435 - 444
  • [42] Sparse Least Squares Support Vector Machines via Genetic Algorithms
    Silva, Juliana Peixoto
    da Rocha Neto, Ajalmar R.
    2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC), 2013, : 248 - 253
  • [43] Imbalanced data classification via support vector machines and genetic algorithms
    Cervantes, Jair
    Li, Xiaoou
    Yu, Wen
    CONNECTION SCIENCE, 2014, 26 (04) : 335 - 348
  • [44] Support vector machines and genetic algorithms for soft-sensing modeling
    Sang Haifeng
    Yuan Weiqi
    Wang Fuli
    He Dakuo
    ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS, 2007, 4493 : 330 - +
  • [45] Evolutionary support vector regression machines
    Stoean, Ruxandra
    Preuss, Mike
    Dumitrescu, D.
    Stoean, Catalin
    SYNASC 2006: EIGHTH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING, PROCEEDINGS, 2007, : 330 - +
  • [46] Analysis of Support Vector Machines Regression
    Tong, Hongzhi
    Chen, Di-Rong
    Peng, Lizhong
    FOUNDATIONS OF COMPUTATIONAL MATHEMATICS, 2009, 9 (02) : 243 - 257
  • [47] Support vector fuzzy regression machines
    Hong, DH
    Hwang, CH
    FUZZY SETS AND SYSTEMS, 2003, 138 (02) : 271 - 281
  • [48] Support Vector Machines for classification and regression
    Brereton, Richard G.
    Lloyd, Gavin R.
    ANALYST, 2010, 135 (02) : 230 - 267
  • [49] Adaptive support vector machines for regression
    Palaniswami, M
    Shilton, A
    ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE, 2002, : 1043 - 1049
  • [50] Nonstationary regression with support vector machines
    Guillermo L. Grinblat
    Lucas C. Uzal
    Pablo F. Verdes
    Pablo M. Granitto
    Neural Computing and Applications, 2015, 26 : 641 - 649
12345