Patent ReferencesMethod and apparatus for remote tissue identification by statistical modeling and hypothesis testing of echo ultrasound signals Dynamic model selection during data compression Rule invocation mechanism for inductive learning engine Automatic target detection process Expert system for assessing accuracy of models of physical phenomena and for selecting alternate models in the presence of noise Case-based knowledge source for artificial intelligence software shell Contact management model assessment system for contact tracking in the presence of model uncertainty and noise Topic discriminator using posterior probability or confidence scores Method and apparatus for preforming mutations in a genetic algorithm-based underwater target tracking system Fuzzy logic-based evidence fusion tool for network analysis InventorsApplicationNo. 301383 filed on 04/27/1999US Classes:703/2, MODELING BY MATHEMATICAL EXPRESSION324/345, By magnetic means382/103, Target tracking or detecting382/240, Pyramid, hierarchy, or tree structure706/12, MACHINE LEARNING706/13, Genetic algorithm and genetic programming system706/47, Ruled-based reasoning system706/49, Blackboard system706/52Reasoning under uncertainty (e.g., fuzzy logic)ExaminersPrimary: Teska, Kevin J.Assistant: Phan, Tho Attorney, Agent or FirmInternational ClassG06F 017/50AbstractA 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.Field of SearchMODELING BY MATHEMATICAL EXPRESSIONELECTRICAL ANALOG SIMULATOR Blackboard system MACHINE LEARNING Genetic algorithm and genetic programming system ADAPTIVE SYSTEM Defuzzification processing Ruled-based reasoning system Reasoning under uncertainty (e.g., fuzzy logic) Target tracking or detecting Sequential decision process (e.g., decision tree structure) Pyramid, hierarchy, or tree structure OF GEOPHYSICAL SURFACE OR SUBSURFACE IN SITU By magnetic means Position indicating (e.g., triangulation) | |