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 of high intensity accelerators is studied by feed-forward back-propagating neural network selfadaptation method. The envelope radius of highintensity proton beam can reach the matched beam radius by selecting suitable control structure of neural network and the linear feedback coefficient, and adjusting the right-coefficient of neural network. The beam halochaos is obviously suppressed and amplitude shake is largely reduced when the neural network self-adaptation control is applied．