Modelling of plasma etching using a generalized regression neural network

Kim, Byungwhan; Kim, Sungmo; Kim, Kunho

doi:10.1016/s0042-207x(03)00075-7

Cited by 55 publications

(28 citation statements)

References 10 publications

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“…Leung et al (Leung et al, 2000) developed a model using GRNN for the purpose of forecasting exchange rate and compares its performance with a variety of forecasting techniques and reported that, apart from having a higher degree of prediction accuracy, GRNN performs statistically better than other evaluated models compared in the study for different currencies. GRNN has also been reported to have performed well in prediction to forecast plant disease (Chtioui et al, 1999), and modelling of plasma etching (Kim et al, 2003).…”

Section: Related Workmentioning

confidence: 98%

Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed - Forword and Generalized Regression Neural Networks

Ajiboye

Arshah

Qin

et al. 2016

IJSECS

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Artificial Neural Network (ANNs) is an efficient machine learning method that can be used to fits model from data for prediction purposes. It is capable of modelling the class prediction as a nonlinear combination of the inputs. However, a number of factors may affect the accuracy of the model created using this approach. The choice of network type and how the network is optimally configured plays important role in the performance of a predictive model created using neural network techniques. This paper compares the accuracy of two typical neural network techniques used for creating a predictive model. The techniques are feed-forward neural network and the generalized regression networks. The model created using both techniques are evaluated for correctness. The resulting outputs show that, the Generalized Regression Neural Network (GRNN) consistently produces a more accurate result. Findings further show that, the fitting of the network predictive model using the technique of Feed-forward Neural Network (FNN) records error value of 1.086 higher than the generalized regression network.

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Section: Related Workmentioning

confidence: 98%

Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed - Forword and Generalized Regression Neural Networks

Ajiboye

Arshah

Qin

et al. 2016

IJSECS

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show abstract

“…x j and x ij are the jth element of x and x i , respectively, f is the so-called spread factor, the optimal value of which is often determined empirically (Kim et al 2003). The function approximation is smoother in the large spread than in the small spread.…”

Section: General Regression Neural Network (Grnn)mentioning

confidence: 99%

Caspian Sea level prediction using satellite altimetry by artificial neural networks

Imani

You

Kuo

2013

Int. J. Environ. Sci. Technol.

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The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. This study focuses on developing and validating a neural methodology applicable to the shortterm forecast of the Caspian Sea level. The input and output data sets used contain two time series obtained from Topex/Poseidon and Jason-1 satellite altimetry missions from 1993 to 2008. The forecast is performed by multilayer perceptron network, radial basis function, and generalized regression neural networks. Several tests of different artificial neural network (ANN) architectures and learning algorithms are carried out as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The results derived from the ANN are compared with observed sea level values and with the forecasts calculated by a routine autoregressive moving average (ARMA) model. Different ANNs satisfactorily provide reliable results for the short-term prediction of Caspian Sea level anomalies. The root mean square errors of the differences between observations and predictions from artificial intelligence approaches can be significantly reduced by about 50 % compared with ARMA techniques.

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“…The ζ is generally regarded as the spread factor, whose optimal value is often determined experimentally. (Kim et al, 2003). If the spread becomes larger, the function approximation will be smoother.…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

A combined generalized regression neural network wavelet model for monthly streamflow prediction

Kişi

2011

KSCE J Civ Eng

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The ability of a combined model, Wavelet-Generalized Regression Neural Network (WGRNN), is investigated in the current study for the prediction of monthly streamflows. The WGRNN model is obtained by combining two methods, Discrete Wavelet Transform (DWT) and Generalized Regression Neural Network (GRNN), for one-month-ahead streamflow forecasting. The monthly flow data of two stations, the Gerdelli Station on the Canakdere River and the Isakoy Station on the Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. The forecasts of the WGRNN model are tested using the Root Mean Square Error (RMSE), Variance Account For (VAF) and correlation coefficient (R) statistics and the results are compared with those of the single GRNN and Feed Forward Neural Network (FFNN). The comparison results revealed that the WGRNN performs better than the GRNN and FFNN models in monthly streamflow prediction. For the Gerdelli and Isakoy stations, it is found that the WGRNN models with RMSE = 5.31 m 3 /s, VAF = 52.3%, R = 0.728 and RMSE = 3.36 m 3 /s, VAF = 55.1%, R = 0.742 in the test period are superior in forecasting monthly streamflows than the best accurate GRNN models with RMSE = 6.39 m 3 /s, VAF = 30.1%, R = 0.553 and RMSE = 4.19 m 3 /s, VAF = 30.1%, R = 0.549, respectively.

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Modelling of plasma etching using a generalized regression neural network

Cited by 55 publications

References 10 publications

Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed - Forword and Generalized Regression Neural Networks

Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed - Forword and Generalized Regression Neural Networks

Caspian Sea level prediction using satellite altimetry by artificial neural networks

A combined generalized regression neural network wavelet model for monthly streamflow prediction

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