To minimize position information extraction inaccuracy while using Position Sensitive Devices (PSD), and to overcome noise jamming resulting from components and signal processing circuits, a Feedback stacking model based on Extreme Learning Machine (FsELM) was proposed. FsELM employed Extreme Learning Machine (ELM) as the basic training block, updated the input data based on the differences between the truth values and monolayer predicted results, developed the feedback stacking models by cyclic training, and realized the effective depth extraction information of the PSD signals. Further, a one-dimensional PSD-based laser triangular displacement detection experiment was designed to evaluate the performance of the algorithm. The processing abilities of traditional filtering methods, such as classical learning algorithm, ELM, its variants and the proposed FsELM were compared. The FsELM exhibited a significantly higher prediction accuracy compared to other processing methods. The mean square error and prediction accuracy are 1.4×10-5 and 0.78%, respectively. In addition, the operating speed of the FsELM is higher than that of all the other methods, except for the models with single training structures, such as ELM. The results demonstrate the efficient management of random noise interference and accurate prediction ability of the FsELM in uncertain environments.