Research On Combat Effectiveness Algorithm Of Flying Weapon Under Dynamic Condition

Nowadays,many kinds of flying weapons are researched and produced.To successfully carry out combat missions,it is significant to choose a certain type of flying weapon that meets the requirements of certain specific indicators.In addition,the evaluation of the combat effectiveness of flying weapons provides the orientation of research and development of flying weapons.Therefore,it is of great inportance to research the combat effectiveness evaluation algorithms of flying weapons.Currently,individual effectiveness evaluation of weapons is researched by many scholars.However,there is little research on the combat effectiveness evaluation of the offensive and defensive confrontation between one weapon system and another weapon system.Therefore,a model of the combat process of flying weapons is established,meanwhile the entire life cycle of the combat process of flying weapons and the individual effectiveness of the flying weapons in the combat process are taken into account.Then the index factors involved in each module of the weapon system are analyzed,and a combat effectiveness evaluation framework system of flying weapons is established,a corresponding data dimensionality reduction model is designed for the data dimension is too high,and neural network methods is used to learn the internal structure of data.An aiding decision system is designed based on combat effectiveness evaluation.The main research contents of this paper are as follows:Firstly.A combat process model for flying weapons under dynamic conditions is established.The research includes the assessment of the viability of flying weapons,the evaluation of anti-detection effectiveness,the evaluation of anti-interception effectiveness,and the evaluation of anti-jamming effectiveness.And the combat model of flying weapons is simulated and analyzed.Based on the Delphi method,the key index factors that affect the combat capability of the flying weapon are extracted.Flying Weapons combat effectiveness evaluation index system is designed based on the Analytic Hierarchy Process.Taking into account the complexity and high dimensionality of the data in the index system,the dimensionality reduction design of the combat effectiveness index system of the flying weapon is performed based on the principal component analysis method,and the errors of the original data and the reconstructed data are analyzed.Secondly.The advantage of the neural network fitting highly nonlinear data is analyzed,and the genetic neural network structure with the ability of genetic algorithm global optimization is designed.The original data set and dimensionality reduction data set are trained based on genetic neural network.The learning error is analyzed and the combat effectiveness evaluation model of flying weapon based on genetic neural network is established.Thirdly.The target of combat aid decision-making for flying weapons is analyzed,a combat effectiveness evaluation and aiding decision system for flying weapons is established,and the advantages of particle swarm optimization in dealing with unconstrained optimization problems is analyzed.If using the weapon index of flying weapons as variables,there are corresponding constraints,considering that the penalty function method is used to convert the constraint optimization problem into an unconstrained optimization problem,the corresponding evaluation function is designed.Then,based on the genetic neural network model,the aiding decision system of the flying weapons is designed.Finally,the decision model is simulated and the decision results are analyzed.