Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the...Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.展开更多
In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with an ε...In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with an ε-insensitive support vector regression. An entropy penalty function is substituted for the plus function to make the objective function con- tinuous ,and a new algorithm involving the Newton-Armijo algorithm proposed to solve the SESVR converge globally to the solution. Theoretically, we give a brief convergence proof to our algorithm. The advantages of our presented algorithm are that we only need to solve a system of linear equations iteratively instead of solving a convex quadratic program, as is the case with a conventional SVR, and lessen the influence of the penalty parameter C in our model. In order to show the efficiency of our algorithm, we employ it to forecast an actual electricity power short-term load. The experimental results show that the presented algorithm, SESVR, plays better precisely and effectively than SVMlight and LIBSVR in stochastic time series forecasting.展开更多
针对轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本的情况,提出了一种基于局部均值分解(local mean decomposition,LMD)的近似熵和支持向量机的轴承故障诊断方法。首先通过LMD方法将非平稳的原始加速度振动信号分解成若干...针对轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本的情况,提出了一种基于局部均值分解(local mean decomposition,LMD)的近似熵和支持向量机的轴承故障诊断方法。首先通过LMD方法将非平稳的原始加速度振动信号分解成若干个平稳的乘积函数(productionfunction,PF);轴承发生不同的故障时,在不同频带内的信号近似熵值会发生改变,故可通过计算不同振动信号的LMD近似熵判断是否发生故障和发生的故障类型;从包含有主要故障信息的PF分量中提取出来的近似熵特征作为输入建立支持向量机(support vector machine,SVM),判断轴承的工作状态和故障类型。展开更多
基金the National Natural Science Foundation of China (60574075)
文摘Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.
文摘In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with an ε-insensitive support vector regression. An entropy penalty function is substituted for the plus function to make the objective function con- tinuous ,and a new algorithm involving the Newton-Armijo algorithm proposed to solve the SESVR converge globally to the solution. Theoretically, we give a brief convergence proof to our algorithm. The advantages of our presented algorithm are that we only need to solve a system of linear equations iteratively instead of solving a convex quadratic program, as is the case with a conventional SVR, and lessen the influence of the penalty parameter C in our model. In order to show the efficiency of our algorithm, we employ it to forecast an actual electricity power short-term load. The experimental results show that the presented algorithm, SESVR, plays better precisely and effectively than SVMlight and LIBSVR in stochastic time series forecasting.
文摘针对轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本的情况,提出了一种基于局部均值分解(local mean decomposition,LMD)的近似熵和支持向量机的轴承故障诊断方法。首先通过LMD方法将非平稳的原始加速度振动信号分解成若干个平稳的乘积函数(productionfunction,PF);轴承发生不同的故障时,在不同频带内的信号近似熵值会发生改变,故可通过计算不同振动信号的LMD近似熵判断是否发生故障和发生的故障类型;从包含有主要故障信息的PF分量中提取出来的近似熵特征作为输入建立支持向量机(support vector machine,SVM),判断轴承的工作状态和故障类型。
