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最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

《中国工程科学》 2008年 第10卷 第11期   页码 89-92

摘要：

根据时间序列近期数据较远期数据包含有更多未来信息的思想，对最小二乘支持向量机预测方法进行了扩展，得到了更具一般性的最小二乘支持向量机预测模型，给出了扩展后的预测模型具体算法。两个时间序列的预测实例表明，扩展后的预测方法获得了更好的预测效果，提升了最小二乘支持向量机预测方法的价值。

关键词： 最小二乘支持向量机     扩展     时间序列     预测    

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

Shiguo XIAO; Shaohong LI

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 871-881 doi: 10.1007/s11709-022-0863-8

摘要： The failure criteria of practical soil mass are very complex, and have significant influence on the safety factor of slope stability. The Coulomb strength criterion and the power-law failure criterion are classically simplified. Each one has limited applicability owing to the noticeable difference between calculated predictions and actual results in some cases. 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 criterion via iteration computation based on strength test data of the soil, which is of more extensive applicability to many problems of slope stability. In particular, three evaluation indexes including coefficient of determination, mean absolute percentage error, and mean square error indicate that fitting precision of the machine learning-based failure criterion is better than those of the linear Coulomb criterion and nonlinear power-law criterion. Based on the proposed LSSVM approach to determine the failure criterion, the limit equilibrium method can be used to calculate the safety factor of three-dimensional slope stability. Analysis of results of the safety factor of two three-dimensional homogeneous slopes shows that the maximum relative errors between the proposed approach and the linear failure criterion-based method and the power-law failure criterion-based method are about 12% and 7%, respectively.

关键词： 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

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： 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

《结构与土木工程前沿（英文）》 2019年 第13卷 第6期   页码 1379-1392 doi: 10.1007/s11709-019-0562-2

摘要： To date, very few researchers employed the Least Square Support Vector Machine (LSSVM) in predicting the resilient modulus ( ) of Unbound Granular Materials (UGMs). This paper focused on the development of a LSSVM model to predict the of recycled materials for pavement applications and comparison with other different models such as Regression, and Artificial Neural Network (ANN). Blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM) with proportions of 100/0, 90/10, 80/20, 70/30, 55/45, 40/60, 20/80, and 0/100 by the total aggregate mass were evaluated for use as UGMs. RCA/RCM materials were collected from dumps on the sides of roads around Mansoura city, Egypt. The investigated blends were evaluated experimentally by routine and advanced tests and the values were determined by Repeated Load Triaxial Test (RLTT). Regression, ANN, and LSSVM models were utilized and compared in predicting the of the investigated blends optimizing the best design model. Results showed that the ’s of the investigated RCA/RCM blends were generally increased with the decrease in RCM proportion. Statistical analyses were utilized for evaluating the performance of the developed models and the inputs sensitivity parameters. Eventually, the results approved that the LSSVM model can be used as a novel tool to estimate the of the investigated RCA/RCM blends.

关键词： Least Square Support Vector Machine     Artificial Neural Network     resilient modulus     Recycled Concrete Aggregate     Recycled Clay Masonry    

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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

《环境科学与工程前沿（英文）》 2019年 第13卷 第2期 doi: 10.1007/s11783-019-1102-y

摘要：

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.

关键词： 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

《机械工程前沿（英文）》 2012年 第7卷 第1期   页码 47-54 doi: 10.1007/s11465-012-0307-6

摘要：

Research of thermal characteristics has been a key issue in the development of high-speed feed system. The thermal positioning error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machine tools. The research work undertaken herein ultimately aims at the development of a comprehensive thermal error identification model with high accuracy and robust. Using multi-class least squares support vector machines (LS-SVM), the thermal positioning error of the feed system is identified with the variance and mean square value of the temperatures of supporting bearings and screw-nut as feature vector. A series of experiments were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 to verify the identification capacity of the presented method. The results show that the recommended model can be used to predict the thermal error of a feed system with good accuracy, which is better than the ordinary BP and RBF neural network. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system.

关键词： 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

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： 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

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 215-239 doi: 10.1007/s11709-018-0489-z

摘要： Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. 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. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

关键词： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric analysis    

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运用支持向量机的稳健智能音频水印设计 Article

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

《信息与电子工程前沿（英文）》 2016年 第17卷 第12期   页码 1320-1330 doi: 10.1631/FITEE.1500297

摘要： 本文提出了一种稳健、智能的音频水印方法，该方法有效地结合了奇异值分解（Singular value decomposition, SVD）和支持向量机（Support vector machine, SVM

关键词： 音频水印；版权保护；奇异值分解；机器学习；支持向量机    

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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

《信息与电子工程前沿（英文）》 2015年 第16卷 第6期   页码 474-485 doi: 10.1631/FITEE.1400295

摘要： Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares 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

《结构与土木工程前沿（英文）》 2022年 第16卷 第3期   页码 347-358 doi: 10.1007/s11709-022-0819-z

摘要： Compressive strength is the most important metric of concrete quality. Various nondestructive and semi-destructive tests can be used to evaluate the compressive strength of concrete. In the present study, a new image-based machine learning method is used to predict concrete compressive strength, including evaluation of six different models. These include support-vector machine model and various deep convolutional neural network models, namely AlexNet, GoogleNet, VGG19, ResNet, and Inception-ResNet-V2. In the present investigation, cement mortar samples were prepared using each of the cement:sand ratios of 1:3, 1:4, and 1:5, and using the water:cement ratios of 0.35 and 0.55. Cement concrete was prepared using the cement:sand:coarse aggregate ratios of 1:5:10, 1:3:6, 1:2:4, 1:1.5:3 and 1:1:2, using the water:cement ratio of 0.5 for all samples. The samples were cut, and several images of the cut surfaces were captured at various zoom levels using a digital microscope. All samples were then tested destructively for compressive strength. The images and corresponding compressive strength were then used to train machine learning models to allow them to predict compressive strength based upon the image data. The Inception-ResNet-V2 models exhibited the best predictions of compressive strength among the models tested. Overall, the present findings validated the use of machine learning models as an efficient means of estimating cement mortar and concrete compressive strengths based on digital microscopic images, as an alternative nondestructive/semi-destructive test method that could be applied at relatively less expense.

关键词： 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

《能源前沿（英文）》 2016年 第10卷 第1期   页码 105-113 doi: 10.1007/s11708-016-0393-y

摘要： This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

关键词： 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

《能源前沿（英文）》 2022年 第16卷 第2期   页码 187-223 doi: 10.1007/s11708-021-0722-7

摘要： 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.

关键词： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine (SVM)    

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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

《机械工程前沿（英文）》 2019年 第14卷 第4期   页码 412-421 doi: 10.1007/s11465-019-0551-0

摘要： A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.

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

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基于随机森林模型的滑动轨迹人机识别 Research Articles

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

《信息与电子工程前沿（英文）》 2019年 第20卷 第7期   页码 925-929 doi: 10.1631/FITEE.1700442

摘要： 识别码在维护网络安全的人机身份验证中得到广泛应用。人机身份验证面临的挑战包括对人与机器滑动轨迹的正确检测。提出一种基于滑动轨迹数据集的人机识别随机森林模型。通过多维性能评价指标，包括识别准确率、识别召回率、识别误报率、识别漏报率、F值和加权准确率，验证该随机森林模型以及基准模型（逻辑回归模型和支持向量机）。随机森林模型多维性能评价指标优于基准模型。

关键词： 人机识别；随机森林；支持向量机；逻辑回归；多维性能评价指标    

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标题 作者 时间 类型 操作

最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

期刊论文

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

Shiguo XIAO; Shaohong LI

期刊论文

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

Pijush SAMUI

期刊论文

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

期刊论文

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

期刊论文

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

Chao JIN, Bo WU, Youmin HU, Yao CHENG

期刊论文

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

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

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

期刊论文

运用支持向量机的稳健智能音频水印设计

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

期刊论文

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

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

期刊论文

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

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

期刊论文

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

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

期刊论文

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

期刊论文

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

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

期刊论文

基于随机森林模型的滑动轨迹人机识别

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

期刊论文