Abstract
Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment.
Highlights
Air target threat assessment refers to comprehensively considering various factors affecting the target threat value, establishing a reasonable indicator system, and quantifying it, and establishing a threat assessment model to evaluate the target threat value
E method of reasoning is to analyze the relationship between index values and threat values and combine expert experience and prior probability to infer the threat values of different targets. is method mainly includes the Bayesian network [1,2,3] and intuitionistic fuzzy logic [4,5,6]. e Bayesian network method can intuitively express the relationship between indicators and threats, but the determination of prior probability depends too much on expert experience. erefore, subjectivity is too strong
The Sphere function is a smooth monotonic function with only one global minimum, which is used to test the convergence speed of the algorithm. e Rosenbrock function is a nonconvex unimodal function with multiple local extremums, which is mainly used to test the convergence speed and optimization accuracy of the improved moth flame optimization (IMFO) Moth position tent initialization Using the training error of gray neural network model (GNNM) as the fitness value, sort to get the order flame position Update moth position Increase Lévy flight for current optimal flame position Get new flame position according to Metropolis criterion Record the current optimal flame fitness value Adaptive reduction in the number of flame
Summary
Air target threat assessment refers to comprehensively considering various factors affecting the target threat value, establishing a reasonable indicator system, and quantifying it, and establishing a threat assessment model to evaluate the target threat value. E method of reasoning is to analyze the relationship between index values and threat values and combine expert experience and prior probability to infer the threat values of different targets. Support vector machine has advantages for small sample prediction, but it has disadvantages of unreasonable index selection and low evaluation accuracy. GNNM is a predictive model that combines the advantages of small sample prediction with gray system theory and the advantages of self-learning of neural network. Erefore, this paper combines the actual situation of air target threat assessment, selects a reasonable indicator system, establishes a threat assessment framework, and scientifically quantifies each indicator. E results show that the proposed model has higher accuracy and better real-time performance in threat assessment. The proposed model is evaluated from accuracy and real-time performance. e results show that the proposed model has higher accuracy and better real-time performance in threat assessment. erefore, this study has certain advantages and important practical significance
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