Machine learning neural network-based damage probability calculation method

Abstract

The invention discloses a damage probability calculation method based on a machine learning neural network. The method comprises the following steps: establishing a damage probability calculation model of a fragment warhead to a target; according to the above damage probability calculation model, a large amount of random calculation is carried out based on a Monte Carlo method for all random parameters influencing the calculation result, and each parameter in each calculation is randomly valued; a neural network in machine learning is used for training a large amount of damage probability example data obtained in the previous step, a damage probability prediction model is obtained, and the damage probability can be predicted according to random parameter values; and using the obtained damage probability prediction model to calculate the average single-shot damage probability of the fragment warhead to the target under the specific missile target intersection condition by adopting a Monte Carlo method. Compared with a conventional method for calculating the damage probability at present, the damage probability calculation method based on machine learning has the advantages that certain accuracy of calculation precision is ensured, the calculation amount of an algorithm is greatly reduced, and the calculation time is greatly shortened.

Description

technical field [0001] The invention belongs to the technical field of damage assessment, in particular to a damage probability calculation method based on a machine learning neural network. Background technique [0002] In order to accurately evaluate the damage effect of the fragmentation warhead on the target, and facilitate the research on the coordination of the battle, it is necessary to establish a corresponding damage probability calculation model on the basis of the target vulnerability analysis. [0003] The commonly used damage probability represents the probability that the warhead will damage the target. The current calculation method of the damage probability of the fragment warhead to the target is too computationally intensive. In MATLAB, it often takes hundreds of seconds or even thousands of seconds to calculate a single result for complex situations. , and the computational complexity grows exponentially with the increase in the number of fragments. When ...

AI Technical Summary

Problems solved by technology

[0003] The damage probability is commonly used to indicate the probability of the warhead damaging the target. The existing calculation method for the damage probability of the fragment warhead to the target is too large. In MATLAB, it often takes hundreds of seconds or even thousands of seconds to calculate a single result for complex situations. , and the amount of computation increases exponentially with the number of fragments

Images