A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

“…The training set was used to optimize the weights and bias of the neurons in each layer, and the accuracy of the ANN was checked using the validation and test sets. 46,64…”

“…The performance of the ANN was evaluated by calculating the r between the ANN-predicted and true antioxidant parameters. 64 It was obtained by: 73–75 where y t i and y p i are the true and predicted antioxidant parameters, respectively, and and are the average values. In eqn (5), the closer the value of r to 1, the better the correlation between the predicted and true antioxidant parameters, and the better the prediction accuracy of the ANN.…”

Children Tracking System Based on ZigBee Wireless Network and Neural Network

“…The BP training technique is one of several methods used to train an ANN. The calculation, BP of error, and a feed-forward input training pattern are all part of BP [35]. An input layer, an output layer, and one or more hidden layers make up the BP-ANN [36].…”

Artificial intelligence‐based super learner approach for prediction and optimization of biodiesel synthesis—A case of waste utilization

“…To overcome these limitations, much attention has been paid to developing novel approaches such as using CeO 2 ‐added fuel, 5 blending diesel with 20% rice bran oil biodiesel 6 and using corrosion inhibitors in biodiesel 3 for the use of biodiesel directly in the diesel engine. The performance of engine and exhaust discharges are also predicted accurately using artificial neural networks (ANNs) 7 . Additionally, life cycle assessment (LCA) has been utilized to assess environmental impacts associated with the biofuels production chain 8 .…”