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

Xiang Xiaodong

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

Abstract:

According to the theory that the present data contains more future information than historical data in time-series,the paper extends the prediction method of least square support vector machine and obtains a more general prediction model of least square support vector machine,and develops algorithm of the extended prediction model.Prediction examples of two time-series show that the extended model is more effective.Therefore it improves the value of the prediction method of least square support vector machine.

Keywords： least square support vector machine     generalization     time series     forecasting    

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应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article

俊红 张,昱 刘

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 272-286 doi: 10.1631/FITEE.1500337

Abstract: 针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。

Keywords： 柴油机；故障诊断；完备集合固有时间尺度分解；最小二乘支持向量机；混合差分进化和粒子群优化算法    

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Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Strategic Study of CAE 2011, Volume 13, Issue 12,   Pages 45-50

Abstract:

The vibration of powerhouse structures is mainly induced by hydraulics factors, mechanical and electromagnetic factors of the generating unit. It nonlinearly couples with the generating unit. Based on prototype observation data of Ertan Hydropower Station, the paper analyzes the coupling effect between vibration of units and powerhouse,and then the vibration response forecasting model of the powerhouse is built based on LS-SVM optimized by particle swarm optimization algorithm, and the prediction results are coincide with the observed data. Further, the paper introduces the running water head as an input divisor into the intelligent prediction model while the forecasting range is extended, and the result is satisfactory.

Keywords： powerhouse     coupled vibration     particle swarm optimization algorithm     least squares support vector machines     response prediction    

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Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error None

Xin CHEN, Ding WANG, Rui-rui LIU, Jie-xin YIN, Ying WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 7,   Pages 917-936 doi: 10.1631/FITEE.1700735

Abstract: Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival (AOA), time-of-arrival (TOA), and frequency-of-arrival (FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares (STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.

Keywords： Single-station     Structural total least squares     Inverse iteration     Angle-of-arrival (AOA)     Time-of-arrival (TOA)     Frequency-of-arrival (FOA)     Disjoint sources    

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Anefficient parallel and distributed solution to nonconvex penalized linear SVMs Personal View

Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4,   Pages 587-603 doi: 10.1631/FITEE.1800566

Abstract: Support vector machines (SVMs) have been recognized as a powerful tool to perform linear classification. When combined with the sparsity-inducing nonconvex penalty, SVMs can perform classification and variable selection simultaneously. However, the nonconvex penalized SVMs in general cannot be solved globally and efficiently due to their nondifferentiability, nonconvexity, and nonsmoothness. Existing solutions to the nonconvex penalized SVMs typically solve this problem in a serial fashion, which are unable to fully use the parallel computing power of modern multi-core machines. On the other hand, the fact that many real-world data are stored in a distributed manner urgently calls for a parallel and distributed solution to the nonconvex penalized SVMs. To circumvent this challenge, we propose an efficient alternating direction method of multipliers (ADMM) based algorithm that solves the nonconvex penalized SVMs in a parallel and distributed way. We design many useful techniques to decrease the computation and synchronization cost of the proposed parallel algorithm. The time complexity analysis demonstrates the low time complexity of the proposed parallel algorithm. Moreover, the convergence of the parallel algorithm is guaranteed. Experimental evaluations on four LIBSVM benchmark datasets demonstrate the efficiency of the proposed parallel algorithm.

Keywords： Linear classification     Support vector machine (SVM)     Nonconvex penalty     Alternating direction method of multipliers (ADMM)     Parallel algorithm    

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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: Rapid growth in information technology and computer networks has resulted in the universal use of data transmission in the digital domain. However, the major challenge faced by digital data owners is protection of data against unauthorized cop-ying and distribution. Digital watermark technology is starting to be considered a credible protection method to mitigate the potential challenges that undermine the efficiency of the system. Digital audio watermarking should retain the quality of the host signal in a way that remains inaudible to the human hearing system. It should be sufficiently robust to be resistant against potential attacks. One of the major deficiencies of conventional audio watermarking techniques is the use of non-intelligent decoders in which some sets of specific rules are used for watermark extraction. This paper presents a new robust intelligent audio water-marking scheme using a synergistic combination of singular value decomposition (SVD) and support vector machine (SVM). The methodology involves embedding a watermark data by modulating the singular values in the SVD transform domain. In the extraction process, an intelligent detector using SVM is suggested for extracting the watermark data. By learning the destructive effects of noise, the detector in question can effectively retrieve the watermark. Diverse experiments under various conditions have been carried out to verify the performance of the proposed scheme. Experimental results showed better imperceptibility, higher robustness, lower payload, and higher operational efficiency, for the proposed method than for conventional techniques.

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

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Predictive Functional Control Based on Fuzzy Model for HVAC Systems

Lu Hongli,Jia Lei,Wang Lei,Gao Rui

Strategic Study of CAE 2006, Volume 8, Issue 9,   Pages 65-68

Abstract:

In HVAC systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainty. A novel predictive functional control strategy based on Takagi-Sugeno fuzzy model was developed in order to settie the difficulty to control HVAC systems. The T - S fuzzy model of controlled process was obtained using the least squares method, then basing on the fuzzy T - S global linear predictive model, the process was controlled by the predictive functional control strategy. Finally, the simulation results in HVAC systems control application showed that the proposed fuzzy model predictive functional control approach improves tracking effect and robustness. Compared with the conventional PID control approaches, this FPFC approach has better dynamical performance such as less overshoot and shorter regulation time, etc.

Keywords： T-S fuzzy model     predictive functional control     least squares method     HVAC systems    

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Nonlinear restoring force identification based on measured time series

Xu Bin,He Jia

Strategic Study of CAE 2011, Volume 13, Issue 9,   Pages 76-82

Abstract:

In this study, a general nonlinear restoring force (NRF) identification approach using structural dynamic response measurements and complete excitations is proposed at first. In this approach, the least-squares technique is employed to identify the parameters of an equivalent linear system of the nonlinear structure model based on the external excitations and the corresponding response measurements. The proposed approach is developed when the structure to be identified is incompletely excited. Both of the approaches are validated with a 4-story frame structure equipped with smart devices of magneto-rheological (MR) damper to simulate nonlinear performance. The identified NRF of the structure is compared with the test measurements. Results show that the proposed data-based approaches are capable of identifying the nonlinear restoring behavior of engineering structures and have the potential to be employed to evaluate the damage initiation and development procedure of engineering structures under dynamic loads.

Keywords： nonlinear restoring force identification     MR damper     least-squares techniques     equivalent linear system     non-parametric model    

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The S-N Curve Fitted by the Least Square Method Considering the Effect of Length of the Confidence Interval

Yang Xiaohua,Jin Ping,Yao Weixing

Strategic Study of CAE 2004, Volume 6, Issue 4,   Pages 41-43

Abstract:

The S-N curve is the base of calculating fatigue structural life. Founding on physical mechanism, this paper presents a weighted least square method in which the weigh of a group of test data is inversely proportional to the length of the confidence interval. The calculating results show that the S-N curve which is gained by the least square method considering the effect of length of the confidence interval is more reliable and secure than the S-N curve which is gained by general least square method.

Keywords： confidence interval     fatigue life     least square method     S-N curve    

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Optical plasma boundary reconstruction based on least squares for EASTTokamak None

Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9,   Pages 1124-1134 doi: 10.1631/FITEE.1700041

Abstract:

Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.

Keywords： Optical boundary reconstruction     Boundary detection     Global contrast     Least square     EAST Tokamak    

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System identification of channel roughness for middle route project of south to north water diversion

Yang Kailin,Wang Yisen

Strategic Study of CAE 2012, Volume 14, Issue 11,   Pages 17-23

Abstract:

This paper presents a new method for the system identification of channel roughness for the water diversion projects. According to the principle of hydraulics, established the relationship among channel roughness, rough height ks and hydraulic radius R, and deduce the linear model by means of the mathematical transformation to make use of the least square method for the identification. Finally, based on the prototype observation data from the south to north water diversion project and considering the influence of channel section shapes, bottom slopes and lengths etc, a universal formula is obtained for calculation of channel roughness by the system identification.

Keywords： channel     roughness     system identification     least square method    

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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 machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F-measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.

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

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Subspace-based identification of discrete time-delay system Article

Qiang LIU,Jia-chen MA

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6,   Pages 566-575 doi: 10.1631/FITEE.1500358

Abstract: We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.

Keywords： Identification problems     Time-delay systems     Subspace identification method     Alternate convex search     Least squares    

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The research on method of geometry-control during erection of the short-line bridge

Wang Min,Zhang Yongtao,Liu Jinghong,Liu Yi,Huang Yue

Strategic Study of CAE 2009, Volume 11, Issue 11,   Pages 79-81

Abstract:

Focus on error control during fabricating and suspended splicing of box girders with segmentation method. After the analysis of the error reasons, and the combination with practical engineering—Sutong B2, it discussed control method, computing method and sensitive analysis method of linear control of prefabricated pre-stressed continuous beam bridge assembly with short line method.

Keywords： geometry-control     error analysis     least square method     erection of the short-line method    

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Performance analysis of new word weighting procedures for opinion mining Article

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1186-1198 doi: 10.1631/FITEE.1500283

Abstract: The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting (IWW). IWW is computed based on the significance of the word in the document (SWD) and the significance of the word in the expression (SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed: (1) Classification performance is enhanced; (2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.

Keywords： Inferred word weight     Opinion mining     Supervised classification     Support vector machine (SVM)     Machine learning    

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Title Author Date Type Operation

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

Xiang Xiaodong

Journal Article

应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

俊红 张,昱 刘

Journal Article

Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Journal Article

Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error

Xin CHEN, Ding WANG, Rui-rui LIU, Jie-xin YIN, Ying WU

Journal Article

Anefficient parallel and distributed solution to nonconvex penalized linear SVMs

Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

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

Journal Article

Predictive Functional Control Based on Fuzzy Model for HVAC Systems

Lu Hongli,Jia Lei,Wang Lei,Gao Rui

Journal Article

Nonlinear restoring force identification based on measured time series

Xu Bin,He Jia

Journal Article

The S-N Curve Fitted by the Least Square Method Considering the Effect of Length of the Confidence Interval

Yang Xiaohua,Jin Ping,Yao Weixing

Journal Article

Optical plasma boundary reconstruction based on least squares for EASTTokamak

Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG

Journal Article

System identification of channel roughness for middle route project of south to north water diversion

Yang Kailin,Wang Yisen

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

Subspace-based identification of discrete time-delay system

Qiang LIU,Jia-chen MA

Journal Article

The research on method of geometry-control during erection of the short-line bridge

Wang Min,Zhang Yongtao,Liu Jinghong,Liu Yi,Huang Yue

Journal Article

Performance analysis of new word weighting procedures for opinion mining

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Journal Article