Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm

Sheng, Li; Dai, Liankui

doi:10.1016/j.fuel.2012.01.001

Cited by 44 publications

(27 citation statements)

References 28 publications

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“…However, ANN has a high requirement for training data. To overcome this, the SVM method is proposed [29][30][31], which can conquer the over-fitting of ANN and is a kind of regression model with excellent performance.…”

Section: Introductionmentioning

confidence: 99%

“…This is time-consuming and the chosen kernel function and the parameters may just be the best one among the tried combinations and probably not be the global optimal solution. To choose a proper kernel function and parameters at a relatively short time, some intelligent algorithms are used [31,35,36]. Compared with LSSVM, the optimized models are better.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term wind power prediction based on LSSVM–GSA model

Yuan

Chen

Yuan

et al. 2015

Energy Conversion and Management

235

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term wind power prediction based on LSSVM–GSA model

Yuan

Chen

Yuan

et al. 2015

Energy Conversion and Management

235

View full text Add to dashboard Cite

“…Later, Suykens et al [13] proposed the least square support vector machine (LSSVM) based on the standard SVM. In that way, the loss function is set to the error square, besides the inequality constraints turned into equality constraints to reduce the parameters to confirm, and the solution of quadratic programming is transformed into solving the linear KKT (Karush-Kuhn-Tucker) equations, which reduces the complexity of the solution and broaden its application [14][15][16]. into high dimensional feature space, and the optimal linear regression function is described as:…”

Section: Methodsmentioning

confidence: 99%

The Application of Joint Optimization Method using GA for Load Forecasting

Qin¹,

Niu²,

Qiu³

et al. 2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

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Abstract. Since load forecasting plays an important role in the planning and operation of power industry, substantial efforts are made in improving the accuracy and reliability of load forecasting. In this paper, we develop a novel hybrid approach based on phase space reconstruction and least square support vector for the short-term load forecasting. However, the proper parameters in phase space reconstruction and least square vector machine have a significant effect on the forecasting performance, and there is no standard solution for the parameter estimation problem. Therefore, in this paper, the genetic algorithm (GA) approach is employed to optimize the parameters of both phase space reconstruction and least square support vector machine together. The experimental results suggest that the joint optimization parameter is superior to the separate optimization solutions.

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“…It was concluded that the latter two are the most effective ones. Other groups used Raman [92] and infrared [93] spectroscopy for classifying gasoline samples with respect to the production facility and brand. Different brands are often characterized by certain additives admixed to the initially obtained petrochemical fraction.…”

Section: Petrochemical Liquid Fuelsmentioning

confidence: 99%

Recent Advances in the Characterization of Gaseous and Liquid Fuels by Vibrational Spectroscopy

Kiefer

2015

Energies

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Most commercial gaseous and liquid fuels are mixtures of multiple chemical compounds. In recent years, these mixtures became even more complicated when the suppliers started to admix biofuels into the petrochemical basic fuels. As the properties of such mixtures can vary with composition, there is a need for reliable analytical technologies in order to ensure stable operation of devices such as internal combustion engines and gas turbines. Vibrational spectroscopic methods have proved their suitability for fuel characterization. Moreover, they have the potential to overcome existing limitations of established technologies, because they are fast and accurate, and they do not require sampling; hence they can be deployed as inline sensors. This article reviews the recent advances of vibrational spectroscopy in terms of infrared absorption (IR) and Raman spectroscopy in the context of fuel characterization. The focus of the paper lies on gaseous and liquid fuels, which are dominant in the transportation sector and in the distributed generation of power. On top of an introduction to the physical principles and review of the literature, the techniques are critically discussed and compared with each other.

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Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm

Cited by 44 publications

References 28 publications

Short-term wind power prediction based on LSSVM–GSA model

Short-term wind power prediction based on LSSVM–GSA model

The Application of Joint Optimization Method using GA for Load Forecasting

Recent Advances in the Characterization of Gaseous and Liquid Fuels by Vibrational Spectroscopy

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