A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting

Kavousi‐Fard, Abdollah; Samet, Haidar; Marzbani, Fatemeh

doi:10.1016/j.eswa.2014.03.053

Cited by 354 publications

(133 citation statements)

References 36 publications

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“…Nonlinear regression model is irreplaceable, so far there are no modeling method, a nonlinear regression model usually use the thought of nonlinear linearization, the nonlinear regression model changes for linear regression model. The nonlinear linearization, however, has proven the variable substitution involves parameter is not set up [21][22].…”

Section: The Traditional Mathematical Regression Modelsmentioning

confidence: 99%

Research on the Security Enhanced Smart Hardware Assisted Regression Analysis and Health Monitoring Technique based Expanded Training Exercise Effect Evaluation Model

Shi¹

2016

IJSIA

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Section: The Traditional Mathematical Regression Modelsmentioning

confidence: 99%

Research on the Security Enhanced Smart Hardware Assisted Regression Analysis and Health Monitoring Technique based Expanded Training Exercise Effect Evaluation Model

Shi¹

2016

IJSIA

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“…The SVR aims to provide a nonlinear mapping function to map the training data {x i , y i ; i = 1,…n} to a high dimensional feature space (Kavousi-Fard et al, 2014). Then, the nonlinear relation can be represented as follows:…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…Recently, SVR, an extension of SVM, has also been receiving increasing attention to solve nonlinear estimation problems. It has been successfully applied in different problems of time series prediction (Kavousi-Fard et al, 2014;Lu et al, 2009;Santamaria-Bonfil et al, 2016;Were et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

Zheng¹,

Pan²

2017

American Journal of Biochemistry and Biotechnology

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Glutamate fermentation is inherently nonlinear, multi-phase and an aerobic fermentation process. As long measurement delays and expensive apparatus cost, on-line measurement of the product concentration is not necessarily available. The present fermentation process monitoring and quality prediction involve manual interpretation of highly informative, however, the concentrations of substrates, biomass and products are only low frequency off-line measurements. In this paper, we propose a novel Multi-Phase Support Vector Regression (MPSVR) based soft sensor model for online quality prediction of glutamate concentration. The glutamate fermentation process can be divided into a sequence of five phases by detecting the trend variation events (also termed as singular points or inflection point) of online measured O 2 in the exhaust gas, the Inflection Point (IP) are easily identified through combining Moving Window (MW) with Pearson Correlation Coefficient (PCC). For each estimation phase, SVR soft sensor model are constructed and their performance is evaluated against fermentation data in a 5 L fermenter. The efficiency of the proposed soft sensor model for online product quality prediction has been demonstrated to be superior compared to that of reported techniques in a 5 L glutamate fermentation process.

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“…SVR is based on extension of the support vector concept proposed by Vapnik et al [31]. The aim of SVR is to provide a nonlinear mapping function to map the training data to a high dimension feature space [32]. The algorithm of …”

Section: Svr For the Prediction Of The Residual Componentsmentioning

confidence: 99%

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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Abstract:As a common device for underwater integrated navigation systems, Doppler velocity log (DVL) has the risk of malfunction. To improve the reliability of navigation systems, a hybrid approach is presented to forecast the measurements of the DVL while it malfunctions. The approach employs partial least squares regression (PLSR) coupled with support vector regression (SVR) to build a hybrid predictor. As the current and past calculating velocities of strapdown inertial navigation system (SINS) are taken as the predictor's inputs, PLSR is applied to cope with the situation where there exists intense relativity among independent variables. Since PLSR is a linear regression, SVR is used to predict the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the hybrid predictor is trained online as a backup, whereas during malfunctions, the predictor offers the estimation of the DVL measurements for information fusion. The performance of the proposed approach is verified with simulations based on SINS/DVL/MCP/pressure sensor (PS) integrated navigation system. The comparison results indicate that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions, thereby improving the navigation accuracy and reliability.

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A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting

Cited by 354 publications

References 36 publications

Research on the Security Enhanced Smart Hardware Assisted Regression Analysis and Health Monitoring Technique based Expanded Training Exercise Effect Evaluation Model

Research on the Security Enhanced Smart Hardware Assisted Regression Analysis and Health Monitoring Technique based Expanded Training Exercise Effect Evaluation Model

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

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