2 results about "Subtractive clustering" patented technology

This invention discloses a method for earthquake landslide hazard zoning based on subtractive clustering ANFIS, belonging to the field of earthquake landslide hazard zoning technology. The method includes the following steps: collecting earthquake landslide monitoring data including seismic data, topographic data, meteorological and hydrological data, and image data; extracting features from the image data to obtain feature-extracted data; based on the earthquake landslide monitoring data, using a subtractive clustering algorithm to determine the initial cluster centers of the earthquake landslide area, thereby obtaining initial clustering information for the earthquake landslide; and combining the earthquake landslide hazard levels and their monitoring data within each geographic grid unit to draw an earthquake landslide hazard zoning map. The data acquisition technology, subtractive clustering algorithm application technology, ANFIS model construction technology, and data calibration technology in this method are closely integrated with modern information technology. The hazard calibration coefficient is used to calibrate the hazard index, improving the accuracy of earthquake landslide hazard assessment.

This invention belongs to the field of design and optimization methods for space traveling wave tubes, specifically an optimization design method for the interaction structure of a space traveling wave tube. Based on the Kriging surrogate model, this invention obtains initial sample points for constructing the Kriging surrogate model using Latin hypercube, considering both the spatial distribution of the sample points and the numerical distribution of the optimization target. It then applies the addition criterion of subtractive clustering algorithm to add multiple new sample points simultaneously in a single iteration, thereby updating the sample point set and reconstructing the surrogate model. This allows the Kriging model to converge with fewer iterations. The resulting model, meeting accuracy requirements, replaces the calculation process of electromagnetic simulation software, reducing the number of simulation calculations and effectively improving the design efficiency of the interaction structure. This provides guidance for subsequent more accurate optimization designs. It effectively solves the problems of long calculation times and high computational resource consumption associated with existing optimization calculations using electromagnetic simulation software.