TY  - JOUR
T1  - In-Line Mixing on Concentration Control Using Computed Multi-Variable Neural Networks Based Techniques
AU - Garcia, R. Ferreiro AU - Rolle, J. Luis Calvo AU - Gomez, M. Romero AU - Catoira, A. DeMiguel 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 7
IS  - 1
SP  - 1
EP  - 10
PY  - 2014
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2014.1.10
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2014.1.10
KW  - Computed variable control
KW  -conjugate gradient algorithm
KW  -feedforward control
KW  -in-line mixer
KW  -feedforward neural networks
KW  -fletcher-river algorithm
KW  -functional approximation
AB  - In-line mixing problems affected by controlled variables, 
  such as conductivity, pH, viscosity which strongly depends on complementary 
  physical variables disturbances, such as temperature and pressure variations 
  are appropriate candidates to be solved using neural network model-based functional 
  approximation techniques. The aim for this type of non-linear 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 in a considered plant. The core of the contribution is a functional approximation 
  approach implemented on the basis of back propagation neural networks associated 
  to the proposed control design strategies (CVFFC and CVFBC).
ER  -