Patent US5740324A successfully refreshed from Google Patents.
US5740324A
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Method for process system identification using neural network
Assignee: Honeywell Inc
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Filing: 1996-07-11
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Granted: 1998-04-14
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Expires: 2016-07-11
Abstract
Abstract The method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trained using a process model to approximate a function which relates process input and output data to process parameter values. Once trained, the network can be used as a system identification tool. In principle, this approach can be used for linear or nonlinear processes, for open or closed loop identification, and for identifying any or all process parameters.
Inventors
- 👤 Anoop Mathur
- 👤 Tariq Samad
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-28 12:24
CPC Classifications
G; G05; G05B; G05B17/00; G05B17/02; Y; Y10; Y10S; Y10S706/00; Y10S706/902; Y10S706/903; Y10S706/906
Claims Analysis
Total Claims: 14
Claim 1
Independent Claim
238 words
A method for making a neural network tool for identifying parameters of a system which is modeled by an equation: ##EQU6## wherein x(t) is a response of the system, t p is a time constant parameter of the system, K p is a gain parameter of the system and θ is a delay parameter of the system, said method comprising the steps of: providing a neural network having an arrangement of processing elements, each of said elements having an input and an output, and adjustable weights connecting the outputs of some of said elements to the inputs of other of said elements, said network having input and output terminal means and target setting terminal means; providing learning algorithm operational means for said network for adjusting said weights wherein output values on said output terminal means are biased to converge respectively to target values applied to said target setting terminal means; making a data process system model of said equation and utilizing said model to generate sets of training data for said neural network with each of said sets having selected values of said parameters within respective predetermined ranges and a resulting response which is said x(t); and sequentially applying said sets of training data to said neural network with each of said sets having said resulting response thereof applied to said input terminal means and said values of said parameters being applied to said target setting terminal means.
Claim 2
Independent Claim
238 words
A method for making a neural network tool for identifying parameters of a system which is modeled by an equation: ##EQU6## wherein x(t) is a response of the system, t p is a time constant parameter of the system, K p is a gain parameter of the system and θ is a delay parameter of the system, said method comprising the steps of: providing a neural network having an arrangement of processing elements, each of said elements having an input and an output, and adjustable weights connecting the outputs of some of said elements to the inputs of other of said elements, said network having input and output terminal means and target setting terminal means; providing learning algorithm operational means for said network for adjusting said weights wherein output values on said output terminal means are biased to converge respectively to target values applied to said target setting terminal means; making a data process system model of said equation and utilizing said model to generate sets of training data for said neural network with each of said sets having selected values of said parameters within respective predetermined ranges and a resulting response which is said x(t); and sequentially applying said sets of training data to said neural network with each of said sets having said resulting response thereof applied to said input terminal means and said values of said parameters being applied to said target setting terminal means.
Claim 3
Dependent Claim
18 words
The method according to claim 1 wherein said system is accurately described by a linear first order equation.
Claim 4
Dependent Claim
31 words
The method according to claim 1 wherein each of said sets of training data applied to said network has only one of said parameters applied to said target setting terminal means.
Claim 5
Dependent Claim
20 words
The method according to claim 3 wherein said one of said parameters is said delay parameter of the system θ.
Claim 6
Dependent Claim
32 words
The method according to claim 1 wherein each of said sets of training data includes a stimulus value for said model which is applied to said input terminal means of said network.
Claim 7
Dependent Claim
14 words
The method according to claim 5 wherein said stimulus value is a step input.
Claim 8
Dependent Claim
245 words
A neural network tool developed by a method for making the neural network tool for identifying parameters of a system which may be modeled by an equation: ##EQU7## wherein x(t) is a response of the system, t p is a time constant parameter of the system, K p is a gain parameter of the system and θ is a delay parameter of the system, said method comprising the steps of: providing a neural network having an arrangement of processing elements, each of said elements having an input and an output, and adjustable weights connecting the outputs of some of said elements to the inputs of other of said elements, said network having input and output terminal means and target setting terminal means; providing learning algorithm operational means for said network for adjusting said weights wherein output values on said output terminal means are biased to converge respectively to target values applied to said target setting terminal means; making a data process system model of said equation and utilizing said model to generate sets of training data for said neural network with each of said sets having selected values of said parameters within respective predetermined ranges and a resulting response which is said x(t); and sequentially applying said sets of training data to said neural network with each of said sets having said resulting response thereof applied to said input terminal means and said values of said parameters being applied to said target setting terminal means.
Claim 9
Dependent Claim
245 words
A neural network tool developed by a method for making the neural network tool for identifying parameters of a system which may be modeled by an equation: ##EQU7## wherein x(t) is a response of the system, t p is a time constant parameter of the system, K p is a gain parameter of the system and θ is a delay parameter of the system, said method comprising the steps of: providing a neural network having an arrangement of processing elements, each of said elements having an input and an output, and adjustable weights connecting the outputs of some of said elements to the inputs of other of said elements, said network having input and output terminal means and target setting terminal means; providing learning algorithm operational means for said network for adjusting said weights wherein output values on said output terminal means are biased to converge respectively to target values applied to said target setting terminal means; making a data process system model of said equation and utilizing said model to generate sets of training data for said neural network with each of said sets having selected values of said parameters within respective predetermined ranges and a resulting response which is said x(t); and sequentially applying said sets of training data to said neural network with each of said sets having said resulting response thereof applied to said input terminal means and said values of said parameters being applied to said target setting terminal means.
Claim 10
Dependent Claim
24 words
The neural network tool developed by the method according to claim 7 wherein said system is accurately described by a linear first order equation.
Claim 11
Dependent Claim
37 words
The neural network tool developed by the method according to claim 7 wherein each of said sets of training data applied to said network has only one of said parameters applied to said target setting terminal means.
Claim 12
Dependent Claim
26 words
The neural network tool developed by the method according to claim 9 wherein said one of said parameters is said delay parameter of the system θ.
Claim 13
Dependent Claim
38 words
The neural network tool developed by the method according to claim 7 wherein each of said sets of training data includes a stimulus value for said model which is applied to said input terminal means of said network.
Claim 14
Dependent Claim
14 words
The method according to claim 11 wherein said stimulus value is a step input.
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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) | 2001-04-13 | 2001-10-13 | 2002-04-14 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-04-13 | 2005-10-13 | 2006-04-14 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-04-13 | 2009-10-13 | 2010-04-14 | $7700.00 | Window Closed / Lapsed |