@inproceedings{scholars598,
            note = {cited By 0; Conference of 2009 International Conference on Signal Acquisition and Processing, ICSAP 2009 ; Conference Date: 3 April 2009 Through 5 April 2009; Conference Code:79615},
             doi = {10.1109/ICSAP.2009.42},
            year = {2009},
         address = {Kuala Lumpur},
           title = {Optimization of neural network model structures for valve stiction modeling},
         journal = {2009 International Conference on Signal Acquisition and Processing, ICSAP 2009},
           pages = {193--197},
        abstract = {Stiction is the most commonly found valve problem in the process industry. Valve stiction may cause oscillations in control loops which increases variability in product quality, accelerates equipment wear and tear, or leads to system instability. To help understand and study the behavior of sticky valve, several valve stiction models have been proposed in the literature. In this paper, a black box Neural Network-based modeling approach is proposed to model valve stiction. It is shown that with optimum model structures, performance of the developed NN stiction model is comparable to other established method. {\^A}{\copyright} 2009 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949992585&doi=10.1109\%2fICSAP.2009.42&partnerID=40&md5=aaacd76650e7d86fafe4f3b5689218a3},
        keywords = {Black boxes; Component; Control valve stiction, neural network, modeling; Control valves; Equipment wear; In-control; Neural network model; Optimum model; Process industries; Product quality; Stiction models, Earthquake resistance; Model structures; Neural networks; Safety valves; Signal analysis; Signal processing; Structural optimization; System stability, Stiction},
            isbn = {9780769535944},
          author = {Zabiri, H. and Mazuki, N.}
}