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Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 89-92

Abstract: more future information than historical data in time-series,the paper extends the prediction method of leastsquare support vector machine and obtains a more general prediction model of least square support vectormachine,and develops algorithm of the extended prediction model.Prediction examples of two time-seriesthat the extended model is more effective.Therefore it improves the value of the prediction method of leastsquare support vector machine.

Keywords： least square support vector machine     generalization     time series     forecasting    

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LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 871-881 doi: 10.1007/s11709-022-0863-8

Abstract: In the work reported here, an analysis method based on the least square support vector machine (LSSVM), a machine learning model, is purposefully provided to establish a complex nonlinear failure criterionevaluation indexes including coefficient of determination, mean absolute percentage error, and mean squareerror indicate that fitting precision of the machine learning-based failure criterion is better than

Keywords： slope stability     safety factor     failure criterion     least square support vector machine    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThe SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRMThe effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques

Mosbeh R. KALOOP, Alaa R. GABR, Sherif M. EL-BADAWY, Ali ARISHA, Sayed SHWALLY, Jong WAN HU

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 6,   Pages 1379-1392 doi: 10.1007/s11709-019-0562-2

Abstract: To date, very few researchers employed the Least Square Support Vector Machine (LSSVM) in predicting

Keywords： Least Square Support Vector Machine     Artificial Neural Network     resilient modulus     Recycled Concrete Aggregate    

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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Frontiers of Environmental Science & Engineering 2019, Volume 13, Issue 2, doi: 10.1007/s11783-019-1102-y

Abstract:

An image-recognition-based diagnosis system of pipe defect types was established.

1043 practical pipe images were gathered by CCTV robot in a southern Chinese city.

The overall accuracy of the system is 84% and the highest accuracy is 99.3%.

The accuracy shows positive correlation to the number of training samples.

Keywords： Sewer pipe defects     Defect diagnosing     Image recognition     Multi-features extraction     Support vector machine    

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Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 1,   Pages 47-54 doi: 10.1007/s11465-012-0307-6

Abstract: error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machineUsing multi-class least squares support vector machines (LS-SVM), the thermal positioning error of thefeed system is identified with the variance and mean square value of the temperatures of supportingbearings and screw-nut as feature vector.

Keywords： least squares support vector machine (LS-SVM)     feed system     thermal error     precision machining    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinesThis study demonstrates the better performance of support vector machines in predicting the strengthThe type of kernel function used in support vector machine models contributed positively to the performance

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: This paper aims to explore two machine learning algorithms including artificial neural network (ANN)and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine    

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A robust intelligent audio watermarking scheme using support vector machine Article

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1320-1330 doi: 10.1631/FITEE.1500297

Abstract: audio water-marking scheme using a synergistic combination of singular value decomposition (SVD) and supportvector machine (SVM).

Keywords： Audio watermarking     Copyright protection     Singular value decomposition (SVD)     Machine learning     Supportvector machine (SVM)    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 474-485 doi: 10.1631/FITEE.1400295

Abstract: regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the leastsquares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported

Keywords： stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Leastsquares support vector machine (LS-SVM)    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted leastsquare (WLS) technique in the restructured electricity markets.

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving entities    

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Keywords： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine    

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Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 4,   Pages 412-421 doi: 10.1007/s11465-019-0551-0

Abstract: predicted by using the characteristic values of performance degradation of products combined with the leastsquares support vector regression algorithm.

Keywords： predecisional remanufacturing     reliability     least squares support vector regression (LS-SVR)     game theory    

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Man-machine verification of mouse trajectory based on the random forestmodel Research Articles

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 7,   Pages 925-929 doi: 10.1631/FITEE.1700442

Abstract:

Identifying code has been widely used in man-machine verification to maintain network security.The challenge in engaging man-machine verification involves the correct classification of man and machineIn this study, we propose a random forest (RF) model for man-machine verification based on the mouseWe also compare the RF model with the baseline models (logistic regression and support vector machine

Keywords： Man-machine verification     Random forest     Support vector machine     Logistic regression     Performance metrics    

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Title Author Date Type Operation

Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Journal Article

LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques

Mosbeh R. KALOOP, Alaa R. GABR, Sherif M. EL-BADAWY, Ali ARISHA, Sayed SHWALLY, Jong WAN HU

Journal Article

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Journal Article

Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

Journal Article

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

Journal Article

Man-machine verification of mouse trajectory based on the random forestmodel

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

Journal Article