Expert system for assessing accuracy of models of physical phenomena and for selecting alternate models in the presence of noise
First Claim
1. A system for assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing a sequence of values of a signal in the presence of noise comprising:
- a residual value generator for generating residual data values reflecting difference values in response to the data sequence and an expected data sequence that is generated in response to a selected model;
a feature estimate value generator for generating feature estimate values of a plurality of predetermined data features in the residual sequence generated by the residual value generator;
a threshold value determination element for generating, in response to the feature estimate values generated by the feature estimate value generator, a threshold value for each feature at an estimated ratio of data to noise;
a feature probability value generator for generating, in response to the threshold value, probability values representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable;
a model selector for selecting a model in response to the probability values generated by the feature probability value generator; and
a controller for controlling the operations of residual value generator, the feature estimate value generator, the threshold value determination element, the feature probability value generator and the model selector in a plurality of iterations, during each iteration the residual value generator using the model selected by the model selection module during the previous iteration.
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Accused Products
Abstract
A system for providing an iterative method of assessing accuracy of selec models of physical phenomena and for determining selection of alternate models in response to a data sequence in the presence of noise. Initially, a residual sequence is generated reflecting difference values between in response to said data sequence and an expected data sequence as would be represented by a selected model. Feature estimate values of a plurality of predetermined data features in the residual sequence are then generated. In response to the feature estimate values, a threshold value is generated for each feature at an estimated ratio of data to noise. Probability values are generated in response to the threshold value, representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable. Finally, a model is selected in response to the probability values for use during a subsequent iteration.
46 Citations
21 Claims
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1. A system for assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing a sequence of values of a signal in the presence of noise comprising:
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a residual value generator for generating residual data values reflecting difference values in response to the data sequence and an expected data sequence that is generated in response to a selected model; a feature estimate value generator for generating feature estimate values of a plurality of predetermined data features in the residual sequence generated by the residual value generator; a threshold value determination element for generating, in response to the feature estimate values generated by the feature estimate value generator, a threshold value for each feature at an estimated ratio of data to noise; a feature probability value generator for generating, in response to the threshold value, probability values representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable; a model selector for selecting a model in response to the probability values generated by the feature probability value generator; and a controller for controlling the operations of residual value generator, the feature estimate value generator, the threshold value determination element, the feature probability value generator and the model selector in a plurality of iterations, during each iteration the residual value generator using the model selected by the model selection module during the previous iteration. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing value of a signal in the presence of noise, the method comprising the steps of iteratively:
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generating a residual sequence reflecting difference values in response to the data sequence and an expected data sequence as would be represented by a selected model; generating feature estimate values of a plurality of predetermined data features in the residual sequence; generating, in response to the feature estimate values, a threshold value for each feature at an estimated ratio of data to noise; generating, in response to the threshold value, probability values representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable; selecting a model in response to the probability values for use during a subsequent iteration. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A system for use in connection with a computer to assess accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing the value of a signal in the presence of noise comprising:
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a residual value generator element for controlling the computer to generate a residual sequence reflecting difference values in response to the data sequence and an expected data sequence as would be represented by a selected model; a feature estimate value generator element for controlling the computer to generate feature estimate values of a plurality of predetermined data features in the residual sequence; a threshold value determination element for controlling the computer to generate, in response to the feature estimate values, a threshold value for each feature at an estimated ratio of data to noise; a feature probability value generator element for controlling the computer to generate, in response to the threshold value, probability values representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable; a model selector element for controlling the computer to select a model in response to the probability values; and a controller for controlling the operations of the computer in response to the residual value generator element, the feature estimate value generator element, the threshold value determination element, the feature probability value generator element and the model selector element in a plurality of iterations, during each iteration the computer in response to the residual value generator element using the model selected by the model selector element during the previous iteration. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification