Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction

By D. Bas, I. Boyaci. 2007

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Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester (2010, background): “Many applications of the ANN for prediction of the biotechnological processes have been reported in the literatures (Manohar and Divakar, 2005; Szaleniec et al. 2006; Bas et al. 2007; Silva et al. 2008; Abdul Rahman et al. 2009).”
A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design (2016, methodology): “In addition to analyzing the effects of each factor, RSM can also generate a mathematical model [3].”
Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles (2011, background): “Artificial neural network (ANN) is a highly simplified model of the structure of a biological network [5].”
Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network (2015, methodology): “The fundamental processing element of ANN (the artificial neuron, unit or nodes) simulates the basic functions of biological neurons [15].”