@inproceedings{scholars9444,
            year = {2018},
         journal = {IOP Conference Series: Materials Science and Engineering},
       publisher = {Institute of Physics Publishing},
             doi = {10.1088/1757-899X/458/1/012054},
          volume = {458},
            note = {cited By 0; Conference of 5th International Conference on Process Engineering and Advanced Materials, ICPEAM 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:143521},
          number = {1},
           title = {IAM: An Intuitive ANFIS-based method for stiction detection},
          author = {Jeremiah, S. S. and Zabiri, H. and Ramasamy, M. and Kamaruddin, B. and Teh, W. K. and Mohd Amiruddin, A. A. A.},
            issn = {17578981},
        abstract = {Stiction in control valves is an industry-wide problem which results in degradation of control performance. A new approach to detect the presence of stiction by utilising only the PV-OP data from control loops is proposed using an Adaptive Neuro-fuzzy Inferencing System (ANFIS). Intuitively, the error between the output of an FIS model developed with stiction and a process with stiction would be minimal. When benchmarked against seventeen well-known industrial control loop case studies, the Intuitive ANFIS-based Method (IAM) accurately predicts the presence or absence of stiction in 65 of loops tested. {\^A}{\copyright} Published under licence by IOP Publishing Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059421549&doi=10.1088\%2f1757-899X\%2f458\%2f1\%2f012054&partnerID=40&md5=47a0de68cdd60388bd5c98f94af41010},
        keywords = {Adaptive control systems; Process engineering; Stiction, Adaptive neuro-fuzzy; Case-studies; Control loop; Control performance; In-control; Industrial controls; New approaches, Fuzzy inference}
}