Adaptive Neuro-fuzzy approach in friction identification

被引：0
|
作者：
Ismail, Muhammad Zaiyad Muda [1 ]
机构：
[1] Univ Teknol MARA, Fak Kejuruteraan Mekan, Shah Alam, Selangor, Malaysia
来源：
4TH INTERNATIONAL CONFERENCE ON MANUFACTURING, OPTIMIZATION, INDUSTRIAL AND MATERIAL ENGINEERING (MOIME 2016) | 2016年 / 131卷
关键词：
D O I：
10.1088/1757-899X/131/1/012015
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
引用
收藏
页数：6
相关论文
共 50 条
  • [31] Adaptive Neuro-Fuzzy Networks with the aid of fuzzy granulation
    Kwak, KC
    Kim, DH
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2005, E88D (09): : 2189 - 2196
  • [32] A Neuro-Fuzzy Identification of ECG Beats
    Chikh, Mohammed Amine
    Ammar, Mohammed
    Marouf, Radja
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2012, 36 (02) : 903 - 914
  • [33] An Adaptive Neuro-Fuzzy Approach for Semantic Analysis of Broadcast Soccer Video
    Hosseini, Monireh-Sadat
    Moghadam, Amir-Masoud Eftekhari
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING AND EPIGENETIC ROBOTICS (ICDL), 2012,
  • [34] Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation
    Petkovic, Dalibor
    Cojbasic, Zarko
    Nikolic, Vlastimir
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 28 : 191 - 195
  • [35] Adaptive Neuro-Fuzzy Approach for Cacao Bean Grading Classification Process
    Brosas, Deborah G.
    Villafuerte, Ritchell S.
    Obediencia, Dindo C.
    [J]. 2020 THIRD INTERNATIONAL CONFERENCE ON VOCATIONAL EDUCATION AND ELECTRICAL ENGINEERING (ICVEE): STRENGTHENING THE FRAMEWORK OF SOCIETY 5.0 THROUGH INNOVATIONS IN EDUCATION, ELECTRICAL, ENGINEERING AND INFORMATICS ENGINEERING, 2020,
  • [36] Analysing livestock farm odour using an adaptive neuro-fuzzy approach
    Pan, Leilei
    Yang, Simon X.
    [J]. BIOSYSTEMS ENGINEERING, 2007, 97 (03) : 387 - 393
  • [37] An Adaptive Neuro-Fuzzy Control Approach for Motion Control of a Robot Arm
    Lakshmi, K. V.
    Mashuq-un-Nabi
    [J]. 2012 INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV), 2012, : 832 - 836
  • [38] Estimation of pile group scour using adaptive neuro-fuzzy approach
    Bateni, S. M.
    Jeng, D.-S.
    [J]. OCEAN ENGINEERING, 2007, 34 (8-9) : 1344 - 1354
  • [39] Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation
    Anicic, Obrad
    Jovic, Srdjan
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 59 : 1111 - 1116
  • [40] An adaptive bidding agent for multiple english auctions:: A neuro-fuzzy approach
    He, MH
    Jennings, NR
    Prügel-Bennett, A
    [J]. 2004 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, PROCEEDINGS, 2004, : 1519 - 1524
12345