目标威胁评估是进行空战任务规划的重要一环。针对传统评估模型中指标信息的不确定性和模糊性, 以多机空战编队整体为出发点, 提出了优化广义回归神经网络的目标威胁评估模型及算法。该优化算法通过遍历散布系数区间内的值, 能迅速找到最优散布系数从而使模型达到最优仿真输出结果。考虑到目前空战多以编队作战为主, 选择目标对我方编队整体的威胁程度作为评价指标, 提高了评估结果的可靠性。最后通过引入实例, 验证了该优化模型的有效性和正确性。

Target threat assessment is very important for air combat mission planning.Due to the uncertainty and fuzziness of target information in traditional assessment model,the model and algorithm for target threat assessment based on improved Generalized Regression Neural Network (GRNN) are proposed,with multi-aircraft air-combat formation as the starting point.This optimization algorithm can quickly find the optimal scatter coefficient through traversing values within the scatter coefficient interval,thus enabling the model to reach the optimal simulation output.Considering that most of the current air-combats are formation operation,the overall threat level of targets to our formation is selected as the assessment criteria,which improves the reliability of assessment results.Finally,an example is given to verify the effectiveness and correctness of this optimization model.