Training Algorithm For Feedforward Neural Networks Research Articles

Performance comparison of feedforward neural network training algorithms in modelling microwave pyrolysis of oil palm fibre for hydrogen and biochar production

AbstractMicrowave pyrolysis of oil palm fibre (OPF) was conducted to study the effect of microwave power, temperature, and nitrogen (N2) flow rate on the production of hydrogen and biochar. The effect of microwave power ranging from 400 to 900 W, reaction temperatures ranging from 450°C to 700°C, and N2 flow rates ranging 200 to 1,200 cm3 min−1 were investigated. The microwave power, reaction temperature, and N2 flow rates are directly proportional to hydrogen and inversely proportional to biochar yield. The characteristics of the biochar were analysed using CHNOS, scanning electron microscopy, and Brunauer–Emmett–Teller results. Central composite design was used to optimize the number of experiment. The optimized experimental datasets were used as inputs for developing a model that can predict the weight of hydrogen and biochar from microwave pyrolysis of OPF. A multilayer feedforward neural network model was developed by comparing the performance of 11 different types of backpropagation training algorithms. The 11 training algorithms belong to six classes, which are (a) additive momentum, (b) self‐adaptive learning rate, (c) resilient backpropagation, (d) conjugate gradient, (e) quasi‐Newton, and (f) Bayesian regulation (BR). Best performing training algorithm was selected based on the lowest error values computed. The objective of this research is to identify the most suitable training algorithm for this process. Levenberg–Marquardt and BR exhibited very good performance. BR exhibited best performance with lowest error values in predicting weight of hydrogen and biochar. The testing data error values for hydrogen weight is 0.216 root mean square error (RMSE), 0.144 mean absolute error (MAE), and 0.020 mean absolute percentage error (MAPE), and biochar is 0.886 RMSE, 0.489 MAE, and 0.019 MAPE. The optimum number of hidden neuron for this algorithm is 19 with mean square error 0.23. The biochar exhibits porous structure suggesting possible land application.

Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization

This paper reports the biopolymerization of e-caprolactone, using lipase Novozyme 435 catalyst at varied impeller speeds and reactor temperatures. A multilayer feedforward neural network (FFNN) model with 11 different training algorithms is developed for the multivariable nonlinear biopolymerization of polycaprolactone (PCL). In previous works, biopolymerization carried out in scaled-up bioreactors is modeled through FFNN. No review discussed the role of different training algorithms in artificial neural network on the estimation of biopolymerization performance. This paper compares mean absolute error, mean square error, and mean absolute percentage error (MAPE) in the PCL biopolymerization process for 11 different training algorithms that belong to six classes, namely (1) additive momentum, (2) self-adaptive learning rate, (3) resilient backpropagation, (4) conjugate gradient backpropagation, (5) quasi-Newton, and (6) Bayesian regulation propagation. This paper aims to identify the most effective training method for biopolymerization. Results show that the quasi-Newton-based and Levenberg–Marquardt algorithms have the best performance with MAPE values of 4.512, 5.31, and 3.21% for the number of average molecular weight, weight average molecular weight, and polydispersity index, respectively.