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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography.
Noel S. Quiming,Noel S. Quiming,Nerissa L. Denola,Nerissa L. Denola,Yoshihiro Saito,Kiyokatsu Jinno +5 more
TL;DR: A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds.
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Comparative Study of Artificial Neural Networks (ANN) and Statistical Methods for Predicting the Performance of Ultrafiltration Process in the Milk Industry
TL;DR: In this article, a comparative study for the prediction of the performance of milk ultrafiltration with ANN and statistical method has been carried out, which reveals that both methods carry out the prediction with a high degree of accuracy.
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Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks
TL;DR: In this paper, the authors present an approach for determining an influence of the type and shape of the input data on the efficiency of such a prediction, based on a selected example of the seismic activity recorded during longwall mining operations conducted in one of the Polish mines.
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Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system
TL;DR: An attempt has been made to develop an artificial neural network model to predict colour in terms of tristimulus values (X, Y, Z) given the concentration of dyes, showing a good level of colour prediction and having the potential to give better predictive performance than the conventional Kubelka–Munk model.
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Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models
TL;DR: Artificial neural network (ANN) and multiple regression methods for modeling the tensile properties of cotton/spandex core-spun yarns are investigated and revealed that ANN has better performance in predicting comparing with multiple linear regression.