基于M-ANFIS-PNN的目标威胁评估模型

目标威胁评估的目的是根据目标的属性和状态信息对目标的威胁程度进行定量估计，为后续作战决策提供辅助支持。现有威胁评估模型大多依赖于数值信息，难以有效处理包含定性、定量数据的目标特征信息。基于此，提出一种改进的自适应模糊神经推理系统模型。在自适应模糊神经推理系统的基础上，引入前件影响矩阵和后件影响矩阵对定性数据进行处理，使得定量、定性数据的影响同时作用于模糊规则的前件参数和后件参数；为了进一步提高模型的输出精度，将自适应模糊神经推理系统的输出层替换为多项式神经网络；通过基于Gower距离的近邻传播聚类算法对改进模型进行结构辨识，确定模糊规则的初始参数。仿真实例验证了所提方法的有效性与可行性，与其他混合属性数据建模方法相比，所提方法具有较高的预测精度，可为作战指挥决策提供有效的辅助支持。

Target threat assessment model based on M-ANFIS-PNN

The purpose of target threat assessment is to quantitatively estimate the threat level of the target based on the target's attributes and status information and provide auxiliary support for operational decision-making. Existing threat assessment models mostly rely on numerical information, and it is difficult to effectively process target feature information containing qualitative and quantitative data. Based on this, this paper proposes an improved adaptive network based fuzzy inference system model. On the basis of the adaptive network based fuzzy inference system, the antecedent influence matrix and the consequent influence matrix are introduced to process the qualitative data, so that the influence of the quantitative and qualitative data acts on the antecedent parameters and the consequent parameters of the fuzzy rules at the same time. In order to further improve the output accuracy of the model, the output layer of the adaptive network based fuzzy inference system is replaced with a polynomial neural network. The structure of the improved model is identified by the affinity propagation clustering algorithm based on Gower distance, and the initial parameters of the fuzzy rules are determined. Simulation examples verify the effectiveness and feasibility of the proposed method. Compared with other mixed attribute data modeling methods, the proposed method has a higher prediction accuracy and can provide effective auxiliary support for battle command decision-making.