On the Use of Quasi-Newton-Based Training of a Feedforward Neural Network for Time Series Forecasting

Devika, P. D.; Achenie, Luke E. K.

doi:10.3233/ifs-1995-3404

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…Thus, various related algorithms have been introduced to address that problem [30]. Most of them are based on second order information about the shape of the error surface [36]. Another problem inherent with neural network training is 'over-fitting' , that is, the error on the training set is driven to a very small value, but when new data are presented to the network the error is large.…”

Section: Research Articlementioning

confidence: 99%

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

Bolanča

Cerjan‐Stefanović

Ukić

et al. 2008

Journal of Chemometrics

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aThe reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%).

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Section: Research Articlementioning

confidence: 99%

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

Bolanča

Cerjan‐Stefanović

Ukić

et al. 2008

Journal of Chemometrics

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Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Bolanča

Cerjan‐Stefanović

Regelja

et al. 2005

J of Separation Science

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Application of artificial neural networks for gradient elution retention modelling in ion chromatographyGradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized twophase training algorithm that enables the researcher to make use of the advantages of first-and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed ANNs are the method of first choice for retention modelling of inorganic anions in IC.

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Modeling batch and column phosphate removal by hydrated ferric oxide-based nanocomposite using response surface methodology and artificial neural network

Zhang

Pan

2014

Chemical Engineering Journal

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On the Use of Quasi-Newton-Based Training of a Feedforward Neural Network for Time Series Forecasting

Cited by 9 publications

References 6 publications

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Modeling batch and column phosphate removal by hydrated ferric oxide-based nanocomposite using response surface methodology and artificial neural network

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