自适应评判神经网络在微分对策中的应用

采用由3个神经网络组成的自适应评判神经网络结构求解微分对策的2点边值问题,其中2个 为控制神经网络,分别实现对微分对策系统中双边控制器的优化,一个为协 态神经网络,用于对2点边值问题中的协态变量进行求解,协态网络的输出对控制网络进行 校正,训练以后的2个控制网络作为双边的反馈控制器在线应用.并将神经网络结果与采用 Chebyshev技术的微分对策数字解进行了对比.追逃微分对策仿真结果表明了该方法的有效 性,并且对初始条件和测量噪声具有较强的鲁棒性. 

Design of Differential Game Controllers Using Adaptive Critic Neural Networks

An adaptive critic structure including three neural networks was developed to solve the two point boundary value problem of differential games.Two control neural networks were used to optimize the controllers on two sides of the differential games,and a co-state neural network was used to approximate the co-state variables in Hamiltonian function.The output of co-state network was used to correct the output of the control networks,and the two convergent control networks can be used as feedback controllers on two sides of the differential games system repectively.The solution of differential games based on neural networks was compared with the one based on Chebyshev technique.The simulation results of the pursing-escaping differential games show that the neural network controllers are consistent with the optimal solution and present good robustness with respect to the initial conditions and measuring noises.