TY  - JOUR
T1  - Observing of pH for Titration Process with Hybrid Neural Network Structure
AU - Asad, Shebel 
JO  - Journal of Engineering and Applied Sciences
VL  - 6
IS  - 5
SP  - 326
EP  - 331
PY  - 2011
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2011.326.331
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2011.326.331
KW  - RBFNN
KW  -pH measurement
KW  -Hybrid neural network
KW  -MLPNN
KW  -Labview
KW  -Matlab
AB  - This study presents the application of a numerical pH observer integrated into titration process as an industrial replacement of real hardware electrodes to measure pH. The proposed observer is designed with Labview and Matlab. First, two kinds of neural networks NN-Multilayer Perceptron network (MLP) and Radial Basis Function network (RBF) are used, separately to design pH observers then to ensure the accuracy and modify the response, a hybrid neural network is developed, it accomplishes the best features found with both MLPNN and RBFNN. The Split-sample method is implemented to select the optimal NN structure. Results are presented and compared in presence of measurement noise (uncertainties in base flow in and temperature variation).
ER  -