Chemometric estimation of post-mortem interval based on Na⁺and K⁺concentrations from human vitreous humour by linear least squares and artificial neural networks modelling

Computational modeling of ionic liquids density by multivariate chemometrics

“…ANNs are widely applied non-linear method in chemometric modeling of complex relationships between variables [69][70][71]. ANNs consist of artificial neurons which implement the essence of biological neurons.…”

Artificial neural network approach to modelling of metal contents in different types of chocolates

“…12,13 The aim of this research was to study the usefulness of chemometric analysis (ANN) in the prediction of the metal contents in different types of chocolate.…”

“…Most common nonlinear activation functions are sigmoid and hyperbolic tangent functions. 13 Coefficients associated with the hidden layer (weights and biases) and coefficients associated with the output layer are grouped separately in sets of matrices. Weights are constantly updated and determined during the training step by means of a learning rule.…”

“…12 The multilayer perceptron (MLP) model made from one input layer, one or more hidden layers and one output layer is the most common flexible and general type of ANNs. 13 MLP represents a feed-forward ANN architecture with unidirectional full connections between successive layers. However, this does not uniquely determine the property of ANN.…”

“…The type of this mathematical function is the activation function itself and it can profoundly influence the performance of the ANN. [13][14][15][16] Thus, it is important to choose a type of activation function for the neurons of a neural network, such as hyperbolic tangent function (tanh), logistic sigmoid func-tion (logistic), identity function, negative exponential function (exponential) and sinusoidal function (sine). Most common nonlinear activation functions are sigmoid and hyperbolic tangent functions.…”