A predictive model for the sensory aroma characteristics of flue-cured tobacco based on a back-propagation neural network

[1] Aghbashlo M(2011)Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm Dry Technol 29 770-779

[2] Kianmehr MH(2018)A new framework of global sensitivity analysis for the chemical kinetic model using PSO-BPNN Comput Chem Eng 112 154-164

[3] Nazghelichi T(2013)Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models Environ Technol 34 2145-2153

[4] Rafiee S(2014)Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality J Agric Food Chem 62 10661-10665

[5] An J(2014)Modeling and computing of stock index forecasting based on neural network and markov chain Stat World J 111 361-366

[6] He G(2017)A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm Proc Comput Sci 60 58-65

[7] Qin F(2017)An improved back propagation neural network prediction model for subsurface drip irrigation system Comput Electr Eng 331 86-100

[8] Li R(2010)Artificial neural networks modeling of contaminated water treatment process by homogeneous and heterogeneous nanocatalysis J Mol Catal A: Chem 17 26-31

[9] Huang Z(2011)Evaluation of smoke quality of C3F flue-cured tobacco from Qujing area using model based on principal component regression Acta Tabacaria Sin 1 84-89

[10] Biswanath M(2019)National flue-cured tobacco leaf scent style division Acta Tabacaria Sin 68 216-221

[1] Aghbashlo M(2011)Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm Dry Technol 29 770-779

[2] Kianmehr MH(2018)A new framework of global sensitivity analysis for the chemical kinetic model using PSO-BPNN Comput Chem Eng 112 154-164

[3] Nazghelichi T(2013)Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models Environ Technol 34 2145-2153

[4] Rafiee S(2014)Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality J Agric Food Chem 62 10661-10665

[5] An J(2014)Modeling and computing of stock index forecasting based on neural network and markov chain Stat World J 111 361-366

[6] He G(2017)A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm Proc Comput Sci 60 58-65

[7] Qin F(2017)An improved back propagation neural network prediction model for subsurface drip irrigation system Comput Electr Eng 331 86-100

[8] Li R(2010)Artificial neural networks modeling of contaminated water treatment process by homogeneous and heterogeneous nanocatalysis J Mol Catal A: Chem 17 26-31

[9] Huang Z(2011)Evaluation of smoke quality of C3F flue-cured tobacco from Qujing area using model based on principal component regression Acta Tabacaria Sin 1 84-89

[10] Biswanath M(2019)National flue-cured tobacco leaf scent style division Acta Tabacaria Sin 68 216-221