Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features

Han, Hongbin; Cui, Xiaoyu; Fan, Yuqiang; Qing, Hong

doi:10.1016/j.applthermaleng.2019.03.111

Cited by 157 publications

(62 citation statements)

References 25 publications

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“…The LSSVM is a different version of the support vector machine (SVM), which was presented by (Suykens &Vandewalle 1999). The LSSVM employs a set of linear equations to increase its convergence speed, while the SVM uses a quadratic programming technique for training (Han et al 2019). The simple structure and high speed of LSSVM convergence make it widely used in regression and classification fields (Han et al 2019, Zhu et al 2019.…”

Section: Least Square Support Vector Machine (Lssvm)mentioning

confidence: 99%

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Jamei¹,

Ahmadianfar²,

Jamei³

et al. 2021

Preprint

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Solar energy is one of the most important renewable energy sources. Assessing the solar potential of area needs analyzed information about the dataset of the measured global solar radiation (GSR). Recently, researches detected the high potential of state-of-the-art artificial intelligence (AI) methods in estimating the GSR successfully. In this study, a novel hybrid AI-based tool consisting of least square support vector machine (LSSVM) integrated with improved simulated annealing (ISA) is proposed to predict the GSR over the Ahvaz synoptic station located in the South-West of Iran. The potential of the proposed hybrid paradigm so-called LSSVM-ISA was evaluated by using multivariate adaptive regression spline (MARS), generalization regression neural network (GRNN), and multivariate linear regression with interactions (MLRI). For precise assessment of efficiency of the AI models, various statistical metrics and validation methods were used to assess the precision of the developed models. A comparison of the obtained results indicated that the LSSVM-ISA method performed better than the MARS, GRNN, and MLRI models. The achieved RMSE values of the MARS, GRNN, and MLRI models were decreased by 9%, 16%, and 30% using the LSSVM-ISA model. Finally, the results demonstrated that the LSSVM-ISA model could be successfully employed for accurately predicting GSR.

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Section: Least Square Support Vector Machine (Lssvm)mentioning

confidence: 99%

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Jamei¹,

Ahmadianfar²,

Jamei³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…LSSVM is a least squares version of the standard SVM within which the model structure is identified by solving a set of linear system instead of a nonlinear optimization problem [53,54]. Similar to the standard SVM, the LSSVM relies on kernel functions to deal with complex and nonlinear datasets [55][56][57]. e LSSVM formulation for pattern classification can be stated as follows [58]:…”

Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Dinh

Hoang

Tran

2021

Applied and Environmental Soil Science

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Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

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“…To overcome these shortcomings, an extended SVM model namely LSSVM comes into being. The LSSVM is a kernelbased machine learning algorithm and has the principle of risk minimization [27]. By solving linear equations instead of solving the quadratic programming problem, it greatly reduces the calculation complexity, and improves the operation efficiency and convergence accuracy.…”

Section: ) Lssvmmentioning

confidence: 99%

A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM

2020

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The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately.

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Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features

Cited by 157 publications

References 25 publications

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM

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