改进遗传神经网络控制混沌运动的研究

用最大Lyapunov指数构造遗传算法中的适应度函数，通过遗传算法优化神经网络的权系数．根据所得到的适应度函数和权系数来构造遗传神经网络控制器，从而提高神经网络控制效果．对离散系统Logistic映射和连续系统Rossler方程、AFM（原子力显微镜）悬臂梁振动系统的混沌运动分别进行了仿真控制．数值实验结果表明本文改进的遗传神经网络控制方法对离散或者连续的混沌系统都能控制到低周期轨道上去，证明了算法的有效性．

Study of chaos control with one improved genetic neural network

The author constructed the fitness functions of genetic algorithms with the maximum Lyapunov exponent,and optimized the weights of a neural network with the genetic algorithm.According to the fitness functions and the weights,a genetic algorithm neural network controller was designed,which improved the efficiency of neural network control.Numerical simulations were performed for the discrete system as Logistic mapping,continuous system as Rossler equations,and the chaotic transition of the AFM microcantilev...