Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance o...Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.展开更多
Dear editor,Space-time adaptive processing(STAP)techniques can effectively suppress strong ground clutter and detect moving target for airborne phased array radar by combing spatial signals and the temporal pulses sim...Dear editor,Space-time adaptive processing(STAP)techniques can effectively suppress strong ground clutter and detect moving target for airborne phased array radar by combing spatial signals and the temporal pulses simultaneously[1].It is known that,when the clutter satisfies the independent and identically-distributed(i.i.d.)condition,the sample matrix inversion(SMI)-based STAP[1]requires twice the number of degree of freedom展开更多
基金Project(513300303)supported by the General Armament Department,China
文摘Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.
基金supported in part by National Natural Science Foundation of China(Grant Nos.61421001,61331021)
文摘Dear editor,Space-time adaptive processing(STAP)techniques can effectively suppress strong ground clutter and detect moving target for airborne phased array radar by combing spatial signals and the temporal pulses simultaneously[1].It is known that,when the clutter satisfies the independent and identically-distributed(i.i.d.)condition,the sample matrix inversion(SMI)-based STAP[1]requires twice the number of degree of freedom
