Abstract:

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.

Key words: threat assessment, adaptive network based fuzzy inference system, polynomial neural network, mixed attribute, affinity propagation clustering algorithm