一种自动目标识别算法参数的优化方法

Parameter Optimization of an Algorithm forAutomatic Target Recognition

提出了一种基于试验设计方法学的响应曲面模型方法，用于建立算法的性能模型．将一种小群体自组织的遗传算法用于算法参数的优化．遗传算法的改进，使得结构更加合理简单，收敛速度明显加快．实验结果表明，所提出的方法能够随着场景条件的变化较好地调整算法参数，从而有效地提高了算法性能．

The performance model of algorithm is developed based on the response surface modellingmethod in the experimental design methodology. A micro-and-self-organized genetic algorithm (MSGA) is proposed to improve performance of the genetic algorithm and to give a better and simplerstructure with a quicker convergence rate for the optimization of the parameters of the algorithm. Experimental results show that the algorithm parameters for the automatic target recognition can be better adjusted with the variation of the scenery conditions by the method proposed.