LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical su...LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.展开更多
Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel base...Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.展开更多
This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be co...This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.展开更多
简述了水声信道与正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统之间的必然联系,概述了基于导频符号的信道估计算法、自适应信道估计算法、子空间信道估计算法以及压缩感知稀疏信道估计方法在水声通信中的应用现...简述了水声信道与正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统之间的必然联系,概述了基于导频符号的信道估计算法、自适应信道估计算法、子空间信道估计算法以及压缩感知稀疏信道估计方法在水声通信中的应用现状,总结了各种算法的特点,比较了各种算法的优缺点,并指出了未来水声信道估计的发展方向。展开更多
TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite ...TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.展开更多
针对相干光正交频分复用(CO-OFDM)系统中光纤的色散和信号传输过程中噪声对系统可靠性的降低,提出了信道的冲激响应加窗(IRM,impulse response processed with window)算法,在最小二乘(LS)算法的基础上,通过时域加窗将信道冲激响应长度...针对相干光正交频分复用(CO-OFDM)系统中光纤的色散和信号传输过程中噪声对系统可靠性的降低,提出了信道的冲激响应加窗(IRM,impulse response processed with window)算法,在最小二乘(LS)算法的基础上,通过时域加窗将信道冲激响应长度以外的噪声滤除,保证了信道冲激响应长度在OFDM保护间隔之内。在算法复杂度提高并不大的前提下,IRW算法系统误码率(BER)比LS算法降低了近1个数量级。仿真结果表明,在256个子载波的CO-OFDM系统中,BER为10-4时,IRW算法对系统信噪比(SNR)的要求比LS算法低了近2.5dB,而只比线性最小均方误差(LMMSE)算法高0.7dB;并且,IRW算法的复乘次数仅比LS算法高8倍,而比LMMSE算法的复乘次数低32倍。展开更多
文摘LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.
文摘Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.
基金Project (No. 2003 AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.
文摘简述了水声信道与正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统之间的必然联系,概述了基于导频符号的信道估计算法、自适应信道估计算法、子空间信道估计算法以及压缩感知稀疏信道估计方法在水声通信中的应用现状,总结了各种算法的特点,比较了各种算法的优缺点,并指出了未来水声信道估计的发展方向。
文摘TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.
文摘针对相干光正交频分复用(CO-OFDM)系统中光纤的色散和信号传输过程中噪声对系统可靠性的降低,提出了信道的冲激响应加窗(IRM,impulse response processed with window)算法,在最小二乘(LS)算法的基础上,通过时域加窗将信道冲激响应长度以外的噪声滤除,保证了信道冲激响应长度在OFDM保护间隔之内。在算法复杂度提高并不大的前提下,IRW算法系统误码率(BER)比LS算法降低了近1个数量级。仿真结果表明,在256个子载波的CO-OFDM系统中,BER为10-4时,IRW算法对系统信噪比(SNR)的要求比LS算法低了近2.5dB,而只比线性最小均方误差(LMMSE)算法高0.7dB;并且,IRW算法的复乘次数仅比LS算法高8倍,而比LMMSE算法的复乘次数低32倍。
