초록 ▼

A method for the selection of hypotheses for modeling physical phenomena, includes detecting if selected features are present by analyzing actual sensed data and parameter values of an initial physical phenomena model; comparing feature estimating hypotheses to the actual data for determining a beli

A method for the selection of hypotheses for modeling physical phenomena, includes detecting if selected features are present by analyzing actual sensed data and parameter values of an initial physical phenomena model; comparing feature estimating hypotheses to the actual data for determining a belief probability assignment value (bpa) for each of the hypotheses which indicates the likelihood that the selected features exist in the actual data and the likelihood that such selected features cannot accurately be determined as existing due to the presence of noise; selecting a set of the hypotheses most accurately modeling the physical phenomena based on the bpa of each selected hypotheses meeting a predetermined criteria; generating evidential support values and lack of evidential support values for subsets of the set having non-zero subset bpa's; ranking the subsets having non-zero subset bpa's in order of decreasing subset bpa; unioning subsets of the power set for forming unioned subsets and determining support values and plausibility values for the unioned subsets; comparing the unioned evidential support values to a predefined threshold value; and using at least one of the unioned subsets having a unioned evidential support value most closely approximating or exceeding the threshold value for selecting alternate models having selected features which more closely approximate the actual data.

대표청구항 ▼

[ What is claimed is:] [1.]1. A method for the selection of hypotheses for modeling physical phenomena, comprising the selection steps of:sensing actual data from said physical phenomena;providing an initial model of said physical phenomena comprising parameter values which represent said actual dat

[ What is claimed is:] [1.]1. A method for the selection of hypotheses for modeling physical phenomena, comprising the selection steps of:sensing actual data from said physical phenomena;providing an initial model of said physical phenomena comprising parameter values which represent said actual data if said actual data was sensed in the absence of noise;detecting if selected features are present by analyzing said actual data and parameter values;extracting said selected features if present using hypotheses for estimating said selected features;comparing said hypotheses to said actual data for determining a belief probability assignment value for each of said hypotheses which indicates the likelihood that said selected features exist in said actual data and the likelihood that such selected features cannot accurately be determined as existing due to the presence of noise and for determining the strength and variance of said estimated selected features as represented by said hypotheses relative to said actual data;selecting a set of said hypotheses believed to most accurately model said physical phenomena based on said belief probability assignment values of said hypotheses meeting a predetermined criteria;determining subsets of said set and a subset belief probability for each of said subsets;generating evidential support values and lack of evidential support values for each of said subsets having non-zero subset belief probability assignment values, wherein said evidential support value is indicative of the amount of confirming evidence for each of said hypotheses and said lack of evidential support value is indicative of a lack of supporting evidence for each of said hypotheses;ranking said subsets having non-zero subset belief probability assignment values in order of decreasing subset belief probability assignment values for forming a power set;unioning subsets of said power set for forming unioned subsets and determining unioned evidential support values for said unioned subsets and unioned belief probability assignment values for said unioned subsets;thresholding said unioned subsets by comparing said unioned evidential support values to a predefined threshold value; andusing at least one of said unioned subsets having a unioned evidential support value most closely approximating or exceeding said threshold value for selecting alternate models having selected features which more closely approximate said actual data.