In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions.The exploration radii in local searches are generated adaptively.Each i...In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions.The exploration radii in local searches are generated adaptively.Each iteration point is selected from some randomly generated trial points according to certain criteria.A restarting strategy is adopted to build the restarting version of the algorithm.The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples.The numerical results suggest that the algorithm and its restarting version are very effective.展开更多
In this paper,we propose a stochastic level-value estimation method to solve a kind of box-constrained global optimization problem.For this purpose,we first derive a generalized variance function associated with the c...In this paper,we propose a stochastic level-value estimation method to solve a kind of box-constrained global optimization problem.For this purpose,we first derive a generalized variance function associated with the considered problem and prove that the largest root of the function is the global minimal value.Then,Newton’s method is applied to find the root.The convergence of the proposed method is established under some suitable conditions.Based on the main idea of the cross-entropy method to update the sampling density function,an important sampling technique is proposed in the implementation.Preliminary numerical experiments indicate the validity of the proposed method.展开更多
针对传统滤波方法在α稳定分布噪声环境下性能退化的问题,从加权Myriad滤波以及加权Merid滤波方法出发,以M估计理论为基础,推导得到稳健加权(robust weighted,RW)滤波方法的统一算法结构,并据此提出了基于RW滤波的新算法,即基于稳健加...针对传统滤波方法在α稳定分布噪声环境下性能退化的问题,从加权Myriad滤波以及加权Merid滤波方法出发,以M估计理论为基础,推导得到稳健加权(robust weighted,RW)滤波方法的统一算法结构,并据此提出了基于RW滤波的新算法,即基于稳健加权滤波的统一框架,从而将加权Myriad、加权Merid以及基于广义柯西分布的加权滤波器统一起来。此外,针对线性调频(linear frequency modulation,LFM)信号采用基于RW的LVD(RW-LVD)方法估计其参数,并根据估计性能对RW方法的抑噪效果进行分析。仿真结果表明,与基于加权Myriad滤波、加权Merid滤波以及基于广义柯西分布的加权滤波等多种方法相比,在强脉冲噪声下RW滤波方法能有效抑制脉冲噪声,并具有良好的稳健性。展开更多
文摘In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions.The exploration radii in local searches are generated adaptively.Each iteration point is selected from some randomly generated trial points according to certain criteria.A restarting strategy is adopted to build the restarting version of the algorithm.The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples.The numerical results suggest that the algorithm and its restarting version are very effective.
文摘In this paper,we propose a stochastic level-value estimation method to solve a kind of box-constrained global optimization problem.For this purpose,we first derive a generalized variance function associated with the considered problem and prove that the largest root of the function is the global minimal value.Then,Newton’s method is applied to find the root.The convergence of the proposed method is established under some suitable conditions.Based on the main idea of the cross-entropy method to update the sampling density function,an important sampling technique is proposed in the implementation.Preliminary numerical experiments indicate the validity of the proposed method.
文摘针对传统滤波方法在α稳定分布噪声环境下性能退化的问题,从加权Myriad滤波以及加权Merid滤波方法出发,以M估计理论为基础,推导得到稳健加权(robust weighted,RW)滤波方法的统一算法结构,并据此提出了基于RW滤波的新算法,即基于稳健加权滤波的统一框架,从而将加权Myriad、加权Merid以及基于广义柯西分布的加权滤波器统一起来。此外,针对线性调频(linear frequency modulation,LFM)信号采用基于RW的LVD(RW-LVD)方法估计其参数,并根据估计性能对RW方法的抑噪效果进行分析。仿真结果表明,与基于加权Myriad滤波、加权Merid滤波以及基于广义柯西分布的加权滤波等多种方法相比,在强脉冲噪声下RW滤波方法能有效抑制脉冲噪声,并具有良好的稳健性。
基金supported by the Natural Science Foundation of Jiangxi Province(20144BAB2110001)Humanities and Social Science Planning Foundation in College of Jiangxi Province(TJ1401)the National Social Science Foundation of China(12BTJ014)
