4 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.

The invention discloses an uncertain industrial process robust fault diagnosis method based on interval distribution analysis. The method comprises the following steps: step 1, establishing an uncertain data interval representation model based on a kernel density estimation model; 2, uncertain process feature extraction based on a complete information principal component analysis algorithm: adopting an uncertain data driving algorithm based on complete information principal component analysis to carry out potential structural information feature mining on uncertain process data expressed in an interval form, therefore, extraction of uncertain fault feature information and multivariate variable dimension reduction are realized. And step 3, constructing a fault diagnosis model based on an interval radial basis function neural network algorithm: constructing a diagnosis model suitable for interval value fault data based on interval fault feature information extracted by a complete information principal component analysis algorithm constructed in the second part, and completing offline modeling of the diagnosis model by adopting a subclustering algorithm.

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.

The invention discloses a seismic landslide risk zoning method based on a subclustering ANFIS, and relates to the technical field of seismic landslide risk zoning, and the method comprises the following steps: collecting seismic landslide monitoring data including seismic data, landform data, meteorological and hydrological data and image data, carrying out the feature extraction of the image data, and obtaining feature extraction data; based on the seismic landslide monitoring data, an initial clustering center point of a seismic landslide area is determined by adopting a subclustering algorithm, and then seismic landslide initial clustering information is acquired; according to the method, a data acquisition technology, a subclustering algorithm application technology, an ANFIS model construction technology and a data calibration technology in the method are closely combined with a modern information technology, and the data acquisition technology, the subclustering algorithm application technology, the ANFIS model construction technology, the data calibration technology and the modern information technology are combined together, so that the earthquake landslide danger level and the earthquake landslide monitoring data in each geographical grid unit are combined together; and the risk index is calibrated by using the risk calibration coefficient, so that the earthquake landslide risk assessment precision is improved.