Prediction of flashover voltage of insulators using least squares support vector machines

Gençoğlu, Muhsin Tunay; Uyar, Murat

doi:10.1016/j.eswa.2009.02.021

Cited by 91 publications

(44 citation statements)

References 18 publications

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“…Because it is a well-known universal estimator, the ANN model can rather be considered as standard benchmark (Gencoglu and Uyar, 2009;Nasr et al, 2002). ANN architecture used is illustrated in Fig.…”

Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

268

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

268

102

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“…In machine learning theory, Support Vector Machines (SVM) is a powerful methodology for providing better prediction results [12]. However, this algorithm is likely to consume too much of memory and computation time, due to the quadratic programming (QP) problem with linear constrain of the SVM formulation.…”

Section: Ls-svm Algorithmmentioning

confidence: 99%

Modeling Based on LS-SVM Method and Predicting Dewatering Ratio in Sludge Process

Luo¹,

Qiao²,

Liao³

2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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Abstract. The sludge produced by treating the urban sewage needs thickening and dewatering and it is important for improving environment to reduce the quantity of discharging sludge by increasing dewatering ratio in sludge process. This paper proposes a novel method that can model least squares support vector machines (LS-SVM) by Soft-sensing to meet the characteristics of multivariable, nonlinearity, large time delay, and time-varying in sludge thickening and dewatering process，and predict the dewatering ratio. The results indicate that the proposed method can not only improve prediction accuracy, but also efficiently get high dewatering ratio by controlling thickening chemicals comparing to the cases using different methods.

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“…The purpose of applying EMD is similar as described in [15] which is to decompose the data sets utilized into a series of different scales of IMF. Meanwhile, in [20], the CV-LSSVM was presented in prediction of flashover voltage of insulators. In the study, several comparisons were performed between different types of kernels, which include RBF kernel and Polynomial kernel.…”

Section: Optimization Of Lssvm Using Cross Validationmentioning

confidence: 99%

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

Yusof¹,

Mustaffa²

2016

IJAIA

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Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.

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Prediction of flashover voltage of insulators using least squares support vector machines

Cited by 91 publications

References 18 publications

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Modeling Based on LS-SVM Method and Predicting Dewatering Ratio in Sludge Process

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

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