针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非...针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法(Spectrum Reduction Algorithm based on Time-Frequency Conversion,SRA-TFC)盲分离接收前端输出信号中的大功率基波信号和其他小功率信号,并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归(Least Squares Support Vector Regression,LS-SVR)算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明:该算法可使宽带接收前端的无杂散失真动态范围(Spurs-Free-Dynamic-Range,SFDR)提高约20 dB,较基于PNM的补偿算法提高了约5 dB.展开更多
In this paper, a new method based on LS-SVM (Least Squares Support Vector Machines) is presented to deal with credit assessment in commercial banks for solving the problem of inadequate samples of the financial data,w...In this paper, a new method based on LS-SVM (Least Squares Support Vector Machines) is presented to deal with credit assessment in commercial banks for solving the problem of inadequate samples of the financial data,which usually happended in most banks in China.On the basis of SLT(Statistical Learning Theory),this approach with methodology of SRM (Structural Risk Minimization)will overcome the shortcomings of traditional credit assessment models,such as over fitting and local optimization,and,by using kernel functions in model,it will effectively solve the problems of linear inseparability and selecting parameters of model.The approach has some good properties including a generalization ability and global optimization in terms of sample processing.It is a new way for the credit assessment on the condition of small samples from bank data.The feasibility,effectiveness and practicability of presented approach was verified by experiments.展开更多
针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该...针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该地区的用电规律,通过引入离散Fréchet距离,建立离散曲线相似性的数学模型,选取出与基准曲线形状相似的相似日,利用相似日负荷数据对LS-SVM预测模型进行训练。经过仿真验证,并与标准LS-SVM模型得到的结果对比,所提预测方法明显提高了预测精度。展开更多
针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取...针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用LS-SVM回归算法实现故障预测。实验结果表明,利用LS-SVM对电路输出平均电压与输出纹波电压的预测相对误差均低于2%,能够跟踪故障特征性能参数的变化趋势,有效实现电力电子电路故障预测。展开更多
文摘针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法(Spectrum Reduction Algorithm based on Time-Frequency Conversion,SRA-TFC)盲分离接收前端输出信号中的大功率基波信号和其他小功率信号,并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归(Least Squares Support Vector Regression,LS-SVR)算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明:该算法可使宽带接收前端的无杂散失真动态范围(Spurs-Free-Dynamic-Range,SFDR)提高约20 dB,较基于PNM的补偿算法提高了约5 dB.
文摘In this paper, a new method based on LS-SVM (Least Squares Support Vector Machines) is presented to deal with credit assessment in commercial banks for solving the problem of inadequate samples of the financial data,which usually happended in most banks in China.On the basis of SLT(Statistical Learning Theory),this approach with methodology of SRM (Structural Risk Minimization)will overcome the shortcomings of traditional credit assessment models,such as over fitting and local optimization,and,by using kernel functions in model,it will effectively solve the problems of linear inseparability and selecting parameters of model.The approach has some good properties including a generalization ability and global optimization in terms of sample processing.It is a new way for the credit assessment on the condition of small samples from bank data.The feasibility,effectiveness and practicability of presented approach was verified by experiments.
文摘针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该地区的用电规律,通过引入离散Fréchet距离,建立离散曲线相似性的数学模型,选取出与基准曲线形状相似的相似日,利用相似日负荷数据对LS-SVM预测模型进行训练。经过仿真验证,并与标准LS-SVM模型得到的结果对比,所提预测方法明显提高了预测精度。
文摘针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用LS-SVM回归算法实现故障预测。实验结果表明,利用LS-SVM对电路输出平均电压与输出纹波电压的预测相对误差均低于2%,能够跟踪故障特征性能参数的变化趋势,有效实现电力电子电路故障预测。
