RESEARCH ON SELF-LEARNING MODEL BASED ON GENETIC ALGORITHMS WITH APPLICATION TO PATH TRACKING IN CGF

摘要：

A self-learning model based on Genetic Algorithms is put forward with application to path tracking in Computer Generated Forces (CGF). On the basis of Agent, the model is constructed to improve the autonomous performance of CGF entities under path tracking environments. First, the framework of the proposed self-learning model is presented. Second, it elaborates the realization, including the principles of condition and action parts of the rule, and the fitness function design. Finally, the parameters and the generalization ability are analyzed in detail. A visible validation system is established to verify the availability and feasibility of the presented self-learning model.

关键词： CGF path tracking GAs Self-learning Agent

作者: YING-NAN ZHAO XIAN-QUAN MENG ZHONG JIN CHUN-MING HOU

作者单位: College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 21 Army Aviation Institute, Beijing 101114, China School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing 210094 College of Physics Science & Information Engineering, Jishou University, Jishou 416000, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1002-1007

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）