Physics based neural network
First Claim
1. A physics based neural network (PBNN) comprising:
- a plurality of nodes each node comprising;
means for receiving at least one input; and
a transfer function for converting said at least one input into an output forming one of said at least one inputs to another one of said plurality of nodes;
at least one training node set comprising said at least one input to one of said plurality of nodes;
at least one input node set comprising said at least one input to said plurality of nodes; and
a training algorithm for adjusting each of said plurality of nodes;
wherein at least one of said transfer functions is different from at least one other of said transfer functions and wherein at least one of said plurality of nodes is a PBNN.
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Abstract
A physics based neural network (PBNN) comprising a plurality of nodes each node comprising structure for receiving at least one input, and a transfer function for converting the at least one input into an output forming one of the at least one inputs to another one of the plurality of nodes, at least one training node set comprising the at least one input to one of the plurality of nodes, at least one input node set comprising the at least one input to the plurality of nodes, and a training algorithm for adjusting each of the plurality of nodes, wherein at least one of the transfer functions is different from at least one other of the transfer functions and wherein at least one of the plurality of nodes is a PBNN.
17 Citations
10 Claims
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1. A physics based neural network (PBNN) comprising:
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a plurality of nodes each node comprising;
means for receiving at least one input; and
a transfer function for converting said at least one input into an output forming one of said at least one inputs to another one of said plurality of nodes;
at least one training node set comprising said at least one input to one of said plurality of nodes;
at least one input node set comprising said at least one input to said plurality of nodes; and
a training algorithm for adjusting each of said plurality of nodes;
wherein at least one of said transfer functions is different from at least one other of said transfer functions and wherein at least one of said plurality of nodes is a PBNN. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of modeling physical systems using physics based neural networks (PBNN) comprising the step of:
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creating a PBNN comprising;
a plurality of nodes each node comprising;
means for receiving at least one input; and
a transfer function for converting said at least one input into an output forming one of said at least one inputs to another one of said plurality of nodes;
at least one training node set comprising said at least one input to one of said plurality of nodes;
at least one input node set comprising said at least one input to said plurality of nodes; and
a training algorithm for adjusting each of said plurality of nodes;
wherein at least one of said transfer functions is different from at least one other of said transfer functions and wherein at least one of said plurality of nodes is a PBNN;
connecting each of said plurality of nodes in accordance with a physical model;
specifying said transfer functions of each of said plurality of nodes;
designating at least one of said plurality of nodes as a training quantity. - View Dependent Claims (9, 10)
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Specification