US5517667A
Expired / Lapsed
US
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Neural network that does not require repetitive training
Assignee: NXP USA Inc
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Filing: 1993-06-14
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Granted: 1996-05-14
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Expires: 2013-06-14
Abstract
Abstract A neural network, which may be implemented either in hardware or software, is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. A hidden neuron in the neural network generates an output based on the product of a plurality of functions. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.
Inventors
- 👤 Shay-Ping T. Wang
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-02 19:43
CPC Classifications
G; G06; G06N; G06N3/00; G06N3/02; G06N3/06; G06N3/063; G06F; G06F7/00; G06F7/38; G06F7/48; G06F7/544; G06F7/552; G06F2207/00; G06F2207/552; G06F2207/5523
Claims Analysis
Total Claims: 11
Claim 1
Independent Claim
139 words
A neural network having a plurality of network inputs and at least one network output, said neural network comprising: a plurality of input nodes, each input node coupled to a corresponding one of said plurality of network inputs and generating corresponding outputs; a plurality of hidden neurons, each hidden neuron having a plurality of neuron inputs and generating an output based on the product of a plurality of functions, wherein each of said plurality of functions is proportional to one of said plurality of neuron inputs raised to the power of one of a plurality of constants; means for distributing said output of each input node to one of said neuron inputs of at least one of said plurality of hidden neurons; and means for summing outputs of said hidden neurons and generating said at least one network output.
Claim 2
Dependent Claim
139 words
A neural network having a plurality of network inputs and at least one network output, said neural network comprising: a plurality of input nodes, each input node coupled to a corresponding one of said plurality of network inputs and generating corresponding outputs; a plurality of hidden neurons, each hidden neuron having a plurality of neuron inputs and generating an output based on the product of a plurality of functions, wherein each of said plurality of functions is proportional to one of said plurality of neuron inputs raised to the power of one of a plurality of constants; means for distributing said output of each input node to one of said neuron inputs of at least one of said plurality of hidden neurons; and means for summing outputs of said hidden neurons and generating said at least one network output.
Claim 3
Dependent Claim
17 words
The neural network recited in claim 1 wherein each of said plurality of constants is an integer.
Claim 4
Dependent Claim
27 words
The neural network recited in claim 2 wherein said at least one network output is based on a weighted sum of the outputs of said hidden neurons.
Claim 5
Dependent Claim
140 words
A neural network having a plurality of network inputs and a plurality of network outputs, said neural network comprising: an input node for receiving successive ones of said plurality of network inputs and generating corresponding node outputs; a plurality of hidden neurons, each hidden neuron having at least one neuron input and generating a neuron output based on a product of a plurality of functions, each of said plurality of functions being a function of said at least one neuron input; means for distributing said node outputs to said at least one neuron input of each of said plurality of hidden neurons; and means for summing neuron outputs and generating said plurality of network outputs, said means for summing including a plurality of summers each summing the outputs of predetermined ones of said hidden neurons and generating said network outputs.
Claim 6
Dependent Claim
140 words
A neural network having a plurality of network inputs and a plurality of network outputs, said neural network comprising: an input node for receiving successive ones of said plurality of network inputs and generating corresponding node outputs; a plurality of hidden neurons, each hidden neuron having at least one neuron input and generating a neuron output based on a product of a plurality of functions, each of said plurality of functions being a function of said at least one neuron input; means for distributing said node outputs to said at least one neuron input of each of said plurality of hidden neurons; and means for summing neuron outputs and generating said plurality of network outputs, said means for summing including a plurality of summers each summing the outputs of predetermined ones of said hidden neurons and generating said network outputs.
Claim 7
Dependent Claim
16 words
The neural network recited in claim 4 wherein said hidden neurons comprise a linear transfer function.
Claim 8
Dependent Claim
28 words
The neural network recited in claim 4 wherein the outputs of at least some of said hidden neurons may be summed by more than one of said summers.
Claim 9
Dependent Claim
19 words
The neural network recited in claim 4 wherein said neural network is contained on at least one integrated circuit.
Claim 10
Dependent Claim
17 words
The neural network recited in claim 4 wherein said neural network is contained in a computer program.
Claim 11
Dependent Claim
32 words
The neural network recited in claim 4 wherein each of said plurality of functions is proportional to said at least one neuron input raised to the power of at least one constant.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1999-05-14 | 1999-11-13 | 2000-05-14 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-05-14 | 2003-11-13 | 2004-05-14 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-05-14 | 2007-11-13 | 2008-05-14 | $7700.00 | Window Closed / Lapsed |