ARTIFICIAL neural networksRADIAL basis functionsSTANDARD deviationsPREDICTION modelsSOILSA time-varying, nonlinear soil-plant system contains many unknown elements that can be quantified based on analytical methodologies. Artificial neural networks (ANNs) are a widely used mathematical computing...
Two different artificial neural network retention models (multi-layer perceptron and radial basis function), three different separation criterion functions (chromatography response function, separation factor product and normalized retention difference product), and four different robustness criterion functions (...
This study presents a computational model that predicts the RUL of water pipelines using an artificial neural network (ANN) model that has been developed using the Levenberg-Marquardt backpropagation algorithm. The model is implemented, tested, and trained using data collected from the city of ...
An artificial neural network (ANN) consists of manyperceptrons. In its simplest form, a perceptron is a function that takes two inputs, multiplies them by two random weights, adds them together with a bias value, passes the results through an activation function and prints the results. The we...
Artificial Intelligence ANN: Artificial Neural Network ARMA: Auto-regressive moving average BP: Back propagation BPNN: Back-Propagation neural network R 2 : Coefficient of determination CI: Confidence Interval CNN: Convolutional Neural Network CNNSVM: Convolutional Support Vector Machine DBN:...
Six single models (analytical hierarchy process (AHP), logistic regression (LR), fuzzy logic (FL), weight of evidence integrated logistic regression (WL), artificial neural network (ANN) and support vector machine (SVM)), were applied to obtain the single landslide susceptibility zonations along ...
A versatile strategy for achieving the second-order advantage when applying different artificial neural networks to non-linear second-order data: Unfolded principal component analysis/residual bilinearization[J] . Alejandro García-Reiriz,Patricia C. Damiani,María J. Culzoni,Héctor C. Goicoechea,...
(QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (...
In addition, it was also found that sample size had a significant effect on both classification and prediction analyzes performed with artificial neural network methods. As a result of the study, it was concluded that with a sample size over 1000, more consistent results can be obtained in the...
2.2. Data-driven models In this study, three different machine learning methods, namely Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were utilized to predict the SAGD production performance. A model had six input parameters (...