The positioning results of terrain matching in flat terrain areas will significantly deteriorate due to the influence of terrain nonlinearity and multibeam measurement noise. To tackle this problem, this study presents the Pulse-Coupled Neural Network (PCNN), which has been effectively utilized for image denoising. The interconnection of surface terrain data nodes is achieved through PCNN ignition, which serves to alleviate the reduction in terrain similarity caused by measurement error. This enables the efficient selection of terrain data, ensuring that points with high measurement accuracy are preserved for terrain matching and positioning operations. The simulation results illustrate that the suggested methodology effectively removes terrain data points with low measurement accuracy, thereby improving the performance of terrain matching and positioning.

The intermittent and volatile nature of wind power generation necessitates thermal power units to provide deep-peak shaving and fast frequency regulation services within the same grid. However, the current proportional integral differential (PID) control performance in the process control foundation of thermal power units falls short of meeting these requirements. Despite the lack of comprehensive research on engineering fastest control (EFC), this article aims to address this gap by studying and analyzing the mechanism and physical defects of the fast tracking filter (FTF), the output tracking input characteristics of EFTF reconstructed by FTF engineering, and the control performance of the constructed EFC. Through mathematical calculation analysis, simulation experiments, and real-world engineering practice, it is concluded that EFC surpasses the limitations of PID control performance and effectively enhances feedback control performance. As a result, it is deemed suitable for process control in thermal power units.