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US4912653A Expired / Lapsed US ⏱️ 0.0 years remaining

Trainable neural network

Assignee: General Dynamics Government Systems Corp
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Filing: 1988-12-14
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Granted: 1990-03-27
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Expires: 2008-12-14
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Abstract

Abstract A trainable artificial neural network includes a computer configured as a plurality of interconnected neural units arranged in a layered network. An input layer has a network input and an output layer has a network output. A neural unit has a first subunit and a second subunit, with the first subunit having one or more first inputs and a corresponding first set of variables for operating upon the said first inputs to provide a first output. The first set of variables can change in response to feedback representing differences between desired network outputs and actual network outputs. The second subunit has a plurality second inputs, and a corresponding second set of variables for operating upon said second inputs to provide a second output. The second set of variables can change in response to differences between desired network outputs for selected network inputs and actual network outputs. The computer provides an activating variable representing the difference between current second output and previous second outputs, and adds the activating variable to said feedback to accelerate the change of the first set of variables.

Inventors

  • 👤 Laurence F. Wood

Bibliographic Details

Application Number —
Jurisdiction US
Data Source google_patents
Last Synchronized 2026-10-23 14:46
CPC Classifications
G; G06; G06N; G06N3/00; G06N3/02; G06N3/08; G06N3/04; G06N3/0499; G06N3/09

Claims Analysis

Total Claims: 3
Claim 1
Independent Claim 186 words
A trainable artificial neural network comprising; a computer configured as a plurality of interconnected neural units arranged in a layered network including an input layer having a network input and an output layer having a network output; at least one of said neural units having a first subunit and a second subunit, said first subunit having one or more first inputs and a corresponding first set of variables for operating upon said first inputs to provide a first output, said first set of variables changeable in response to feedback representing differences between desired network outputs and actual network outputs; said second subunit having a plurality of second inputs, and a corresponding second set of variables for operating upon said second inputs to provide a second output, said second set of variables changeable in response to differences between desired network outputs for selected network inputs and actual network outputs, and means for providing an activating variable representing the difference between current second output and previous second outputs; and means for adding said activating variable to said feedback to accelerate the change of said first set of variables.
Claim 2
Dependent Claim 186 words
A trainable artificial neural network comprising; a computer configured as a plurality of interconnected neural units arranged in a layered network including an input layer having a network input and an output layer having a network output; at least one of said neural units having a first subunit and a second subunit, said first subunit having one or more first inputs and a corresponding first set of variables for operating upon said first inputs to provide a first output, said first set of variables changeable in response to feedback representing differences between desired network outputs and actual network outputs; said second subunit having a plurality of second inputs, and a corresponding second set of variables for operating upon said second inputs to provide a second output, said second set of variables changeable in response to differences between desired network outputs for selected network inputs and actual network outputs, and means for providing an activating variable representing the difference between current second output and previous second outputs; and means for adding said activating variable to said feedback to accelerate the change of said first set of variables.
Claim 3
Dependent Claim 33 words
The neural network of claim 1 wherein said first set of variables has a first time constant, said second set of variables has a second time constant longer than said first time constant.

References Cited (Backward Citations)

US3533072A Cited
US3950733A Cited
US4254474A Cited
US4326259A Cited
US4660166A Cited
US4807168A Cited

USPTO Annuity & Maintenance Windows

Annuity Stage Window Opens Payment Deadline Surcharge Period Until Estimated Fee Status
3.5 Years (Year 4) 1993-03-26 1993-09-25 1994-03-27 $2000.00 Window Closed / Lapsed
7.5 Years (Year 8) 1997-03-26 1997-09-25 1998-03-27 $3760.00 Window Closed / Lapsed
11.5 Years (Year 12) 2001-03-26 2001-09-25 2002-03-27 $7700.00 Window Closed / Lapsed

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