束晕-混沌的神经网络自适应控制

首先研究了强流离子束在周期磁场聚焦通道中传输时产生的束晕-混沌动力学行为,采用的周期磁场聚焦强度形式为与实际相近的余弦函数形式。然后利用神经网络方法对非线性复杂系统控制的优越性,提出前馈反传神经网络方法对强流离子束中束晕-混沌进行自适应控制。通过适当选择的神经网络控制结构和线性反馈系数以及自适应调整神经网络的权系数,可将强流离子束的包络半径达到束匹配半径的控制目标,且束包络的抖动大小明显减少,同时束晕-混沌现象得到了明显的抑制。

Control of Beam Halo-Chaos Using Neural Network Self-adaptation Method

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied.