| 摘 要: 电子战无人机的作战效能评估在未来智能网信体系作中具有重要意义。针对电子战无人机作战效能评估过程中影响因素复杂、小样本、非线性等问题，引入了支持向量机算法，为了提高评估的效率和有效性，引入具有较强伪随机性、自身规律性的混沌系统对粒子群初始粒子进行了优化，然后利用混沌粒子群对支持向量机的参数进行了优选，提高了整体评估效率。仿真实验结果表明混沌粒子群-支持向量机模型可以准确地对电子战无人机进行作战效能评估，具有较好的计算精度。 |
| 关键词: 电子战无人机；作战效能评估；混沌粒子群；支持向量机 |
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中图分类号: TP301
文献标识码: A
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| Operational Effectiveness Evaluation of Electronic Warfare UAV based on Chaotic Particle Swarm Optimization Support Vector Machine |
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MA Xingmin, ZHANG Yong
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(System Second Department, North China Institute of Computing Technology, Beijing 100083, China )
maxingmin1983@163.com; 576156365@qq.com
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| Abstract: The combat effectiveness evaluation of electronic warfare UAVs is of great significance in the future intelligent network information system. Aiming at the existing problems of complex influencing factors, small samples, and nonlinearity, the support vector machine algorithm is considered. In order to improve the efficiency and effectiveness of the evaluation, strong pseudo-random and self-regularity chaotic system firstly optimizes initial particles of the particle swarm, and then uses the chaotic particle swarm to optimize the parameters of the support vector machine, which improved the overall evaluation efficiency. The simulation experiment results show that the chaotic particle swarm-support vector machine model can accurately evaluate the combat effectiveness of electronic warfare UAVs, and has good calculation accuracy. |
| Keywords: electronic warfare UAV; combat effectiveness evaluation; chaotic particle swarm; support vector machine |
