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公开(公告)号：US20190228256A1
公开(公告)日：2019-07-25
申请号：US16251819
申请日：2019-01-18
Applicant: Raytheon Company
Inventor： Marcus Alton Teter , Natalie Rae Plotkin , Scott Allen Imhoff , Walter Parish Gililland, JR. , Austin Jay Jorgensen
Abstract: A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.
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公开(公告)号：US11544492B2
公开(公告)日：2023-01-03
申请号：US16251819
申请日：2019-01-18
Applicant: Raytheon Company
Inventor： Marcus Alton Teter , Natalie Rae Plotkin , Scott Allen Imhoff , Walter Parish Gililland, Jr. , Austin Jay Jorgensen
Abstract: A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.
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