An analysis of formability of aluminium preforms using neural network

Poshal, G.; Ganesan, P.

doi:10.1016/j.jmatprotec.2007.11.107

Cited by 27 publications

(11 citation statements)

References 14 publications

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“…While effort has been paid to tune for some optimal value of σ [46], for its determination in general, the common width can be a set by some multiple of the average distance between the basis centres [28,47]. The variance (i.e., spread value) may also be adjusted by using iteration methods [48,49], but it often involves expensive computation.…”

Section: Selection Of Spread Parametermentioning

confidence: 99%

Enhanced radial basis function neural networks for ozone level estimation

Wahid

H.³

et al. 2015

Neurocomputing

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Section: Selection Of Spread Parametermentioning

confidence: 99%

Enhanced radial basis function neural networks for ozone level estimation

Wahid

H.³

et al. 2015

Neurocomputing

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“…The level of correlation between the data can be measured by determining the correlation coefficient [19,20]. Correlation coefficient equal to one in the cross plot, means a perfect linear correspondence between targets and outputs.…”

Section: Ann Modelingmentioning

confidence: 99%

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

2013

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“…Therefore, many authors suggested that RBFNN offers good performance in terms of accuracy, efficiency and simplicity (e.g. in [8][9]). The RBFNN structure available in the Matlab neural network toolbox [10] is used in the current analysis.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Computational Intelligence Estimation of Natural Background Ozone Level and its Distribution for Air Quality Modelling and Emission Control

Wahid¹,

Ha²,

Duc³

2011

28th International Symposium on Automation and Robotics in Construction (ISARC 2011)

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ABSTRACT:Background ozone, known as the ozone that occurs in the troposphere as a result of biogenic emissions without photochemical influences, has a close relationship with human health risk. The prediction of the background ozone level by an air quality model could cover a wider region, whereas a measurement method can only record at monitoring sites. The problem is that simulation with deterministic models is quite tedious because of the nonlinear nature of some particular chemical reactions involved in the pollutant formulation. In this work, we present a reliable method for determination of the background ozone using the ambient measurement data. Our proposed definition can be used to determine the background level at any part of the globe and in any seasons without relying on data obtained at remote sites.A statistical model approach will be used for the estimation of the background ozone concentration, and a method for extrapolating the site data will be utilised to approximate the spatial distribution on the region. The proposed method will be applied in the Sydney basin to evaluate its effectiveness in background ozone determination. The results show the advantage of the proposed approach as a globally generic and computationally efficient way for the background ozone estimation with a reasonable accuracy.

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An analysis of formability of aluminium preforms using neural network

Cited by 27 publications

References 14 publications

Enhanced radial basis function neural networks for ozone level estimation

Enhanced radial basis function neural networks for ozone level estimation

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

Computational Intelligence Estimation of Natural Background Ozone Level and its Distribution for Air Quality Modelling and Emission Control

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