神经网络理论在微分对策中的应用

阐述了微分对策理论在实际应用中存在的问题,并从动态冲突的实际过程出发,将神经网络理论与分层式智能控制理论相结合,来模型化微分对策问题.将微分对策的双向优化问题转化成基于神经网络理论的目标机动性辨识和单向优化控制问题,将微分对策的定性与定量结合起来,为有效加入人的经验提供了一种技术途径.并采用自适应分布式学习速率,大大提高了神经网络的学习速度和精度.

Application of Neural Networks in Differential Game

The existing problems of application of differential game in practice are analyzed.Considering the processes of practical dynamic conflicts,neural networks combined with the theory of multilayer intelligence control are introduced to modeling the problem of differential game.It turns the problem of differential game into the problem of maneuver identification and optimization control based on neural networks.This process of solving differential games is more conformity with practice,and provides an efficient method for adding human experience and combining the qualitative analysis with quantitative analysis of differential game.The adaptive distributed learning rate of neural networks is developed to increase the learning speed and performance greatly.