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A TCR Measurement Method Based on High and Low Temperature Data Fusion Using an Improved Particle Swarm Optimization Algorithm

ActiveCN121682648BSolve FragmentationSolve the problem of dimension mismatchEnsemble learningBiological modelsDigital dataDecision maker
This invention relates to the field of electrical digital data processing technology, specifically a TCR measurement method based on high and low temperature region data fusion using an improved particle swarm optimization algorithm. The method inputs the original time-series data from the high and low temperature regions, removes noise and outliers using isolated forest and Grubbs criterion, and maps the data to a unified temperature domain grid using piecewise Lagrange interpolation. An ergodic sequence is generated using Logistic chaotic mapping to initialize the particle swarm position vector, which is then transformed into node coordinate parameters in geometric space, and a cubic basis spline fitting curve is constructed. Particle fitness is evaluated using the sum of squared residuals and curvature smoothness as constraints. During iteration, the average Euclidean distance from the particle to the swarm centroid is calculated to generate a spatial dispersion factor. A multi-interval weighted decision maker based on the Mamdani framework is used to identify probe, development, convergence, and escape states, dynamically adjusting the inertia weights accordingly. This effectively solves the problems of data fragmentation and premature convergence, ultimately outputting a continuous and smooth resistance temperature coefficient distribution across the entire temperature domain.
Owner:HAINING YUNHUANG NEW MATERIALS CO LTD