US5574827A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Method of operating a neural network
Assignee: NXP USA Inc
•
Filing: 1995-04-17
•
Granted: 1996-11-12
•
Expires: 2015-04-17
Abstract
Abstract A method is implemented in hardware or software type neural network, the neural network is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. Each neuron applying a gating function to each of neural network inputs. 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
2026-11-05 03:39
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: 13
Claim 1
Independent Claim
117 words
A method of operating a neural network having a plurality of neural network inputs, a plurality of neurons for inputting a plurality of inputs and generating at least one output, said method comprising the steps of: (a) distributing each of said neural network inputs to each of said neuron inputs; (b) each neuron applying a gating function to each of said neural network inputs to produce corresponding gated inputs; (c) each neuron multiplying each of said gated inputs together to generate a product; (d) each neuron multiplying said product by a weight value to generate a neuron output for each neuron; and (e) summing the outputs of said neurons to generate said at least one network output.
Claim 2
Independent Claim
117 words
A method of operating a neural network having a plurality of neural network inputs, a plurality of neurons for inputting a plurality of inputs and generating at least one output, said method comprising the steps of: (a) distributing each of said neural network inputs to each of said neuron inputs; (b) each neuron applying a gating function to each of said neural network inputs to produce corresponding gated inputs; (c) each neuron multiplying each of said gated inputs together to generate a product; (d) each neuron multiplying said product by a weight value to generate a neuron output for each neuron; and (e) summing the outputs of said neurons to generate said at least one network output.
Claim 3
Dependent Claim
23 words
The method of operating a neural network in claim 1 wherein the operation of said neural network is based upon a polynomial expansion.
Claim 4
Dependent Claim
87 words
The method of operating a neural network in claim 2 wherein said polynomial expansion has the form: ##EQU4## wherein y represents the output of the neural network; wherein w i-1 represents the weight value for the ith neuron; wherein x 1 , x 2 , . . . , x n represent inputs to said neural network; wherein g 1i , . . . g ni represent gating functions for the ith neuron which are applied to said inputs; and wherein n and m are positive integers.
Claim 5
Dependent Claim
41 words
The method of operating a neural network in claim 3 wherein each x i is represented by the function x i =f i (zj), wherein zj is any arbitrary variable, and wherein the indices i and j are any positive integers.
Claim 6
Dependent Claim
27 words
The method of operating a neural network in claim 3 wherein the operation of said neural network is based upon a truncated version of said polynomial expansion.
Claim 7
Dependent Claim
114 words
The method of training a neutral network in claim 3 wherein said polynomial expansion has the form: ##EQU5## wherein y represents the output of the neural network; wherein W 0 , w 1 , . . . w N-1 represents weight values, N being a positive integer representing the Nth neuron, and the term w i in general representing the weight value associated with the i+1 neuron; wherein x 1 , x 2 , . . ., x n represent inputs to said neural network, n being a positive integer; and wherein g 1N , . . . , g nN represents grating functions for the Nth neuron which are applied to said inputs.
Claim 8
Dependent Claim
71 words
The method of training a neural network in claim 2 wherein said polynomial expansion has the form: ##EQU6## wherein y represents the output of the neural network; wherein w i-1 represents the weight value for the ith neuron; wherein x i , x j , x k represent inputs to said neural network; wherein n is a positive integer; and wherein f 1 (i) through f 6 (i,j,k) are index-generating functions.
Claim 9
Dependent Claim
22 words
The method of operating a neutral network in claim 1 wherein the operation of said network is based upon an orthogonal function.
Claim 10
Dependent Claim
38 words
The method of operating a neural network in claim 8 wherein said orthogonal function is a sine, cosine, exponential, logarithmic, Fourier transformation, Legendre polynomial, or a non-linear function such as a Volterra function or a radial basis function.
Claim 11
Dependent Claim
28 words
The method of operating a neural network in claim 1 wherein the operation of said neural network is based upon a combination of polynomial expansion and orthogonal functions.
Claim 12
Dependent Claim
22 words
The method of operating a neural network in claim 1 wherein said neural network is contained on at least one integrated circuit.
Claim 13
Dependent Claim
20 words
The method of operating a neural network in claim 1 wherein said neural network is contained in a computer program.
References Cited (Backward Citations)
US3638196A
Cited
US3967100A
Cited
US3922536A
Cited
US4156922A
Cited
US4660166A
Cited
WO1987007053A1
Cited
US4949292A
Cited
US4874963A
Cited
US5333239A
Cited
US4912649A
Cited
US5046020A
Cited
US5333240A
Cited
US5293457A
Cited
WO1990015390A1
Cited
US5103405A
Cited
US5073867A
Cited
US5042001A
Cited
US5210825A
Cited
US5058180A
Cited
US5166539A
Cited
US5159660A
Cited
US5267151A
Cited
US5265192A
Cited
US5239594A
Cited
US5359700A
Cited
US5263121A
Cited
US5278945A
Cited
US5251286A
Cited
US5323470A
Cited
US5412670A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1999-11-12 | 2000-05-13 | 2000-11-12 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-11-12 | 2004-05-13 | 2004-11-12 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-11-12 | 2008-05-13 | 2008-11-12 | $7700.00 | Window Closed / Lapsed |