@article{MAKHILLAJIT2006595209,
    title = {Physical Modeling and Control of Electric Arc Furnace by Neural Inverse Model},
    journal = {Asian Journal of Information Technology},
    volume = {5},
    number = {9},
    pages = {1028-1033},
    year = {2006},
    issn = {1682-3915},
    doi = {ajit.2006.1028.1033},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2006.1028.1033},
    author = {M.M. Lafifi,B. Boulebtateche,S. Afifi,Z. Oualia,R. Lakel,M. Bedda and},
    keywords = {Electric arc furnace,fusion,neural inverse model,multi-layer neural network,process control},
    abstract = {In this study, we try to answer a certain number of questions raised in the electric arc furnace control.  In this study, we mention the studied choice of the physical model which approaches best the Electric Arc Furnace (E.A.F).  The nonlinear model uses the conductance like electric parameter of  adjustment, whereas usually one uses the resistance of arc as  parameter of adjustment although its accessibility remains implicit.  In order to answer the preset objectives, i.e. to control the process  of the E.A.F. and this with an acceptable stability in the respect of the  constraints and minimization of energy, it is proposed a structure of  control by neural inverse  model implemented by a Multi-Layer Perceptron  network (MLP).  The technique of training data base  uses the retro propagation of error gradient algorithm. An application on MATLAB SIMULINK made it possible to simulate this structure;  convincing results are recorded.}
    }