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This paper describes the simulation of the global orbit feedback system using the singular value decomposition (SVD) method, the error minimization method, and the neural network method. Instead of facing unacceptable correction result raised occasionally in the SVD method, we choose the error minimization method for the global orbit feedback. This method provides minimum orbit errors while avoiding unacceptable corrections, and keeps the orbit within the dynamic aperture of the storage ring. We simulate the Pohang Light Source (PLS) storage ring using the Methodical Accelerator Design (MAD) code that generates the orbit distortions for the error minimization method and the learning data set for neural network method. In order to compare the effectiveness of the neural network method with others, a neural network is trained by the learning algorithm using the learning data set. The global response matrix with a minimum error and the trained neural network are used to the global orbit feedback system. The simulation shows that a selection of beam position monitors (BPMs) is very sensitive in the reduction of rms orbit distortions, and the random choice gives better results than any other cases.

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