Research And Realization Of Weapon Effectiveness Evaluation Model And Its Self-Learning

With the development of science and technology, a large number of high-tech weapons used in naval warfare field, marine environment elements, at the same time also greatly affect the weapon effectiveness. Research on operational effectiveness of naval weapons under the specific meteorological and hydrographic elements of the marine environment, is of great significance to raise the operative level of weapons and to better support military operations decisions. In addition, at the early of database established, weapon operation is short of information. It is a common research goal of many scholars to solve the weapon effectiveness evaluation problem under this kind of circumstance, at the same time, to improve the evaluation precision of the model and the reference. So the thesis conducts a series of research on these problems.Support vector regression machine can well deal with weapons in the effectiveness evaluation of small samples, nonlinear and high dimension problems, but operational environment elements significantly affect the weapon effectiveness and the influence degree of different elements are not nearly the same. In order to take full advantage of the importance of differences of the environmental elements and to improve the evaluation accuracy, the thesis proposes an elements weighted support vector regression machine (WSVR) evaluation model. The model uses a combination weighting method based on G1 subjective method and grey relation analysis objective method to get the weights of environmental elements and improves the SVR model, and the algorithm implementation steps are given. At the end, the thesis utilizes example analysis and comparing with other models to verify the performance of the model.In order to improve the evaluation precision of the model, it is necessary to execute model parameters optimization. To overcome deficiencies of standard particle swarm optimization (PSO) algorithm, such as easily being lost in local optimum, the slow evolutionary convergence speed and poor search accuracy and so on, and to improve the global convergence of the PSO algorithm, In the thesis, combining simulated annealing (SA) algorithm and the chaotic (C) theory, an improved chaotic simulated annealing particle swarm algorithm(CSA-PSO) is proposed, and the flowchart and realization steps of the algorithm is provided. Then, the thesis test and validate the performance of algorithms based on the example analysis and uses the algorithm to optimize the parameters of WSVR model.At last, in view of effective evaluation problem of weapons which are short of data information, the thesis introduces derivative reasoning mechanism. Using the sample data of one or more other weapons having a high similarity with the weapon to be evaluated, and adaptively choosing the most suitable model from the general model library, the special evaluation model of weapon is obtained by training the model with the sample. In order to improve the evaluation precision and reliability, the thesis introduces self-learning mechanism of the evaluation model, executing secondary training of the model according to certain rules and updating the special model to complete the self-learning. At the final of the thesis, based on the UML modeling and.Net three-layer architecture model, the function module design and implementation of the derivative reasoning of weapon efficiency and self-learning of the model have been completed.