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
For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optim...For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optimal.In order to obtain the optimal design of the system,it is necessary to integrate the physical system and its controller.Taking component parameters and energy management strategy as research objects,this paper elaborates an integrated optimization approach for a series–parallel hybrid electric vehicle.Firstly,a rule-based control strategy that can be applied online is designed according to various driving modes of the hybrid electric vehicle.Then,considering the coupling between component parameters and control strategies,a dual-layer optimization framework with genetic algorithm and double dynamic programming is proposed to optimize fuel economy and battery life.Among them,parameters of component size and control for the upper-layer of the framework are selected as preparative optimization parameters.In order to get rid of the influences of energy management strategies and obtain the optimal upper-layer parameters,the lower-layer of the framework adopts the global optimization algorithm to calculate the optimal energy distribution ratio for each driving mode.The results indicate that,while ensuring the good working condition of the battery,the fuel economy has improved by 7.79%under the selected driving cycle after optimization.The optimized upper-layer parameters combined with the proposed control rules can be applied online.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For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optimal.In order to obtain the optimal design of the system,it is necessary to integrate the physical system and its controller.Taking component parameters and energy management strategy as research objects,this paper elaborates an integrated optimization approach for a series–parallel hybrid electric vehicle.Firstly,a rule-based control strategy that can be applied online is designed according to various driving modes of the hybrid electric vehicle.Then,considering the coupling between component parameters and control strategies,a dual-layer optimization framework with genetic algorithm and double dynamic programming is proposed to optimize fuel economy and battery life.Among them,parameters of component size and control for the upper-layer of the framework are selected as preparative optimization parameters.In order to get rid of the influences of energy management strategies and obtain the optimal upper-layer parameters,the lower-layer of the framework adopts the global optimization algorithm to calculate the optimal energy distribution ratio for each driving mode.The results indicate that,while ensuring the good working condition of the battery,the fuel economy has improved by 7.79%under the selected driving cycle after optimization.The optimized upper-layer parameters combined with the proposed control rules can be applied online.
