@article{MAKHILLRJAS2010549052,
    title = {Computed Multi-Variable Control Applied on Viscosity Control by Means of In-line Mixing Control Using Neural Networks},
    journal = {Research Journal of Applied Sciences},
    volume = {5},
    number = {4},
    pages = {274-282},
    year = {2010},
    issn = {1815-932x},
    doi = {rjasci.2010.274.282},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2010.274.282},
    author = {Ramon,Alberto DeMiguel and},
    keywords = {In-line mixing,computed variable control,functional approximation,feedforward neural networks,conjugate gradient algorithm,multivariable control},
    abstract = {Model-based functional approximation techniques are being used on in-line mixing processes affected by controlled variables which depend strongly on complementary physical variables. The aim for such type of nonlinear control problems is to compute the proportions of input product flow rates yielding a final product thus satisfying as much physical properties as manipulated input flow rates exists. The core of the contribution is based on functional approximation implemented by means of backpropagation neural networks associated to the computed multivariable control strategy on a viscosity control problem.}
    }