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
Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the light of the functional analysis approach,the variational simultaneous physical retrieval model (VSPRM)has been developed.Its appro...Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the light of the functional analysis approach,the variational simultaneous physical retrieval model (VSPRM)has been developed.Its approximation of 1st degree is VSPRM1,which is identical with the ISPRM.Its approximation of 2nd degree is VSPRM2,more advanced than the VSPRM1. This paper has analyzed the function of VSPRM2,pointing out the potentiality of synergy retrieval of this model.Also,it has dealt with the problem of parameterization of water vapor's kernel functions and retrieval of water vapor remote sensing. Because of the characteristics of this strong ill posed inverse problem,prior information must be used wisely in order to get the accurate calculation of radiance R.In the previous paper,we discussed how to build the best first guess field,the way to determine the P_s and to correct the calculation of radiance.In this paper,we continue discussing in depth about the calculation of transmittance,the determination of surface parameters and the selection for an optimum combination of channels for the low-level sounding. The long-term experiment and comparison work under operational environment have shown that the ISPRM is useful for retrieval of temperature and water vapor parameters over China including the Tibetan Plateau,and it further proves the scientific nature of well-posed inverse theory.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.
基金NNSF of China(49794030#).National"973"No.4(G1998040909#)and 863-308(863-2-7-4-12#).
文摘Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the light of the functional analysis approach,the variational simultaneous physical retrieval model (VSPRM)has been developed.Its approximation of 1st degree is VSPRM1,which is identical with the ISPRM.Its approximation of 2nd degree is VSPRM2,more advanced than the VSPRM1. This paper has analyzed the function of VSPRM2,pointing out the potentiality of synergy retrieval of this model.Also,it has dealt with the problem of parameterization of water vapor's kernel functions and retrieval of water vapor remote sensing. Because of the characteristics of this strong ill posed inverse problem,prior information must be used wisely in order to get the accurate calculation of radiance R.In the previous paper,we discussed how to build the best first guess field,the way to determine the P_s and to correct the calculation of radiance.In this paper,we continue discussing in depth about the calculation of transmittance,the determination of surface parameters and the selection for an optimum combination of channels for the low-level sounding. The long-term experiment and comparison work under operational environment have shown that the ISPRM is useful for retrieval of temperature and water vapor parameters over China including the Tibetan Plateau,and it further proves the scientific nature of well-posed inverse theory.
