Particle Back Propagation Neural Network Optimization Algorithm for CMTA

摘要：

Cooperative multiple target attack(CMTA) is one of the most important part in air combat, many optimization methods such as heuristic algorithm, ant colony algorithm have been used to solve the problem. This study proposes a novel algorithm that is Particle Back Propagation neural network Optimization (PBPO) for solving decision making problem of air combat CMTA. PBPO algorithm took the basic idea of Particle Swarm Optimization (PSO) algorithm, such as particles, global best position, past best position and moving particles position to get best solution. Instead of using conventional PSO formula to compute particle position, BPNN (Back Propagation Neural Network) was constructed and trained to get a new particle position. Using PBPO to solve the decision making problem in CMTA, simulation result shows that the novel algorithm has a good performance.

关键词： cooperative multiple target attack back propagation neural network optimization particle swarm optimization decision making

作者: Deng Yingcan Li Ni

作者单位: School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Future Information Technology(2010年未来信息技术国际会议 ICFIT 2010)

会议地点: 长沙

会议语种:英文

页码: 425-428

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）