In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to div...In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing （SA） algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.展开更多
Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is intr...Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.展开更多
基金Supported by the National Natural Science Foundation of China(91016004)
文摘In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing （SA） algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.
基金supported by the Fundamental Research Funds for the Central Universities(No.NS2016027)
文摘Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.
