Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique

2009 ◽  
Vol 47 (1) ◽  
pp. 46-59 ◽  
Author(s):  
S. Sivasankaran ◽  
R. Narayanasamy ◽  
T. Ramesh ◽  
M. Prabhakar
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Statistical Technique ◽  
Backpropagation Neural Network

Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model

RSC Advances ◽  
2015 ◽  
Vol 5 (101) ◽  
pp. 82654-82665 ◽  
Author(s):  
Ghaidaa S. Daood ◽  
Hamidon Basri ◽  
Johnson Stanslas ◽  
Hamid Reza Fard Masoumi ◽  
Mahiran Basri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Statistical Technique ◽  
Brain Delivery ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique.

The Application of Genetic Algorithm Backpropagation Neural Network Model on the Prediction and Optimization of Wastewater Treatment System

2013 ◽  
Vol 838-841 ◽  
pp. 2525-2531
Author(s):  
Fu Quan Jia ◽  
Zhang Wei He ◽  
Zhu Jun Tian ◽  
Zhao Bo Chen ◽  
Hong Cheng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Correlation Coefficients ◽  
Treatment System ◽  
Backpropagation Neural Network ◽  
Predictive Values ◽  
Wastewater Treatment System

Prediction and optimization on water quality parameters (WQPs) have become more and more important to the wastewater treatment system (WWTs). In this study, the genetic algorithm backpropagation neural network model (GA-BPNN) had been used to predict and optimize WQPs of a low-strengthen complex wastewater treatment system (LSCWWTs). Results showed that the correlation coefficients between the predicted values and measured values were R2 =0.946 for COD, R2=0.962 for BOD, R2=0.933 for TN, R2=0.985 for NH3-N, R2=0.969 for TP, and R2=0.968 for SS, indicating the predictive values by the GA-BPNN model well fitted the mesured values of effluent WQPs. The optimal effluent WQPs were COD=27.6mg/L, BOD=7.1mg/L, TN=5.4mg/L, NH3-N=0.9mg/L, TP=0.11mg/L and SS=9.25mg/L, respectively. And the corresponding operating parameters were MLSS=3045.4mg/L, MLVSS=2405.9mg/L, T=23.2 °C, R=1.4, SRT=12.5d, HRT=17.3h, CODin =643.3mg/L, BODin=342.2mg/L, TNin=54.2mg/L, NH3-Nin=45.3mg/L, TPin=4.9mg/L, SSin=452.6mg/L, which could be beneficial to the operation optimization of LSCWWTs.

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