Neural network design for engineering applications

Rafiq, Muhammad; Bugmann, Guido; Easterbrook, Dave

doi:10.1016/s0045-7949(01)00039-6

Cited by 605 publications

(310 citation statements)

References 9 publications

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“…[35]). The same transformation is also required for the elements of the target vectors d. A function, through which the elements of input and the target vectors of the data set attain values in the range [-1,1], was selected in the present study [32].…”

Section: (E) Normalization Functions For the Input And Target Vectorsmentioning

confidence: 99%

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Morfidis¹,

Kostinakis²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Section: (E) Normalization Functions For the Input And Target Vectorsmentioning

confidence: 99%

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Morfidis¹,

Kostinakis²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Among activation functions, the sigmoid (logistic) function is the one most usually employed in ANN applications. It is given by (Jenkins, 1997;Rafiq et al, 2001;Hambli et al, 2006):…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The training process in ANNs involves presenting a set of examples (input patterns) with known outputs (target output) (Jenkins, 1997;Rafiq et al, 2001;Hambli et al, 2006;Hambli, 2009) A total of 64 measurements were performed. The proportion of ANN training set from the available data ranged generally from 25% to 80%.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

Hattab

Hambli

Motelica-Heino

et al. 2013

Journal of Geochemical Exploration

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The assessment of chromium concentrations in plants requires the quantification of a large number of soil factors that affect their potential availability and subsequent toxicity and a mathematical model that predicts their relative concentrations. Many soil characteristics can change the availability of chromium (Cr) to plants in soils. However, accurate, rapid and simple predictive models of metal concentrations are still lacking in soil and plant analysis. In the present work a novel artificial neural network (ANN) model was developed as an alternative rapid and accurate tool for the prediction of Cr concentration in dwarf bean leaves grown in the laboratory on phytoremediated contaminated soils treated with different amendments. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in south-western France. Second, a series of measurements were performed on the soil samples.The inputs are the soil amendment, the soil pH, the soil electrical conductivity and the dissolved organic carbon of the soil and the output is the concentration of Cr in the dwarf bean 2 leaves. Third, an ANN model was developed and its performance was evaluated using a test data set and then applied to predict the exposition of the bean leaves to the Cr concentration versus the soil inputs. The performance of the ANN method was compared with the traditional multi linear regressions method using the training and test data sets. The results of this studyshow that the ANN model trained on experimental measurements can be successfully applied to the rapid prediction of plant exposition to Cr.

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“…The dependent variables were the collector efficiency and the collector heat output. A similar approach can be found in [29]. In the second phase, the optimal ANN architecture was selected and trained.…”

Section: Simulation Designmentioning

confidence: 99%

Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors

Facão

Varga

Oliveira

2004

International Journal of Green Energy

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In the last decade, artificial neural networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, speed and non-linearity. This paper describes an alternative approach to assess two types of hybrid solar collector/heat pipe systems (plate heat pipe type and tube heat pipe type) using ANNs.Multiple Layer Perceptrons (MLPs) and Radial Basis Networks (RBFs) were considered. The networks were trained using results from mathematical models generated by Monte Carlo simulation. The mathematical models were based on energy balances and resulted in a system of non-linear equations. The solution of the models was very sensitive to initial estimates, and convergence was not obtained under certain conditions. Between the two neural models, MLPs performed slightly better than RBFs. It can be concluded that similar configurations were adequate for both collector systems. It was found that ANNs simulated both collector efficiency and heat output with high accuracy when "unseen" data were presented to the networks. An important advantage of a trained ANN over the mathematical models is that convergence is not an issue and the result is obtained almost instantaneously.

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Neural network design for engineering applications

Cited by 605 publications

References 9 publications

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors

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