Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal ke...Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.展开更多
The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging re...The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning.In this study,we present a novel method for simultaneous optimization of the SVR kernel function and its parameters,formulated as a mixed integer optimization problem and solved using the recently proposed heuristic 'extremal optimization (EO)'.We present the problem formulation for the optimization of the SVR kernel and parameters,the EO-SVR algorithm,and experimental tests with five benchmark regression problems.The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.展开更多
学习机器性能是决定智能位移反分析效果的关键,针对现有智能反分析存在的问题,将高斯过程回归(Gaussian Process Regression,简称GPR)引入隧道工程计算模型参数的反演,并采用单一各向同性核函数之和作为GPR的组合核函数以提高其泛化性...学习机器性能是决定智能位移反分析效果的关键,针对现有智能反分析存在的问题,将高斯过程回归(Gaussian Process Regression,简称GPR)引入隧道工程计算模型参数的反演,并采用单一各向同性核函数之和作为GPR的组合核函数以提高其泛化性能。为克服传统共轭梯度法优化求取最优GPR超参数的缺陷,改用十进制遗传算法替代共轭梯度法在训练过程中搜索GPR最优超参数,并编制了相应的计算程序。结合北口隧道施工监测进行了算法程序的应用,并与进化–单一核函数高斯过程回归算法和进化支持向量回归(SVR)算法的应用结果作了对比,结果表明本文提出的进化高斯过程算法显著提高了反演精度,可以应用于岩土工程计算模型参数的反演辨识,并为类似工程提供了借鉴。展开更多
基金supported by National Natural Science Foundation under Grant No.50875247Shanxi Province Natural Science Foundation under Grant No.2009011026-1
文摘Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.
文摘The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning.In this study,we present a novel method for simultaneous optimization of the SVR kernel function and its parameters,formulated as a mixed integer optimization problem and solved using the recently proposed heuristic 'extremal optimization (EO)'.We present the problem formulation for the optimization of the SVR kernel and parameters,the EO-SVR algorithm,and experimental tests with five benchmark regression problems.The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.
文摘学习机器性能是决定智能位移反分析效果的关键,针对现有智能反分析存在的问题,将高斯过程回归(Gaussian Process Regression,简称GPR)引入隧道工程计算模型参数的反演,并采用单一各向同性核函数之和作为GPR的组合核函数以提高其泛化性能。为克服传统共轭梯度法优化求取最优GPR超参数的缺陷,改用十进制遗传算法替代共轭梯度法在训练过程中搜索GPR最优超参数,并编制了相应的计算程序。结合北口隧道施工监测进行了算法程序的应用,并与进化–单一核函数高斯过程回归算法和进化支持向量回归(SVR)算法的应用结果作了对比,结果表明本文提出的进化高斯过程算法显著提高了反演精度,可以应用于岩土工程计算模型参数的反演辨识,并为类似工程提供了借鉴。
