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US5455890A
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Method for structuring an expert system utilizing one or more neural networks
Assignee: Motorola Solutions Inc
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Filing: 1993-09-30
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Granted: 1995-10-03
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Expires: 2013-09-30
Abstract
Abstract Neural networks learn expert system rules, for either business or real-time applications, to improve the robustness and speed of execution of the expert system. One or more neural networks are constructed which incorporate the production rules of one or more expert systems. Each neural network is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. Each 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 Wang
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-25 07:20
CPC Classifications
G; G06; G06N; G06N3/00; G06N3/02; G06N3/04; G06N3/042; Y; Y10; Y10S; Y10S128/00; Y10S128/92; Y10S128/925
Claims Analysis
Total Claims: 44
Claim 1
Independent Claim
179 words
A method for implementing production rules of an expert system using a neural network, said method comprising the steps of: (a) defining a plurality of inputs and system outputs for said expert system; (b) defining at least one group of production rules which define a relationship between one of said system outputs and at least one of said plurality of inputs; (c) defining said neural network, said neural network having at least one network output, said neural network being responsive to said at least one of said plurality of inputs corresponding to said at least one group of production rules, and said neural network comprising a plurality of neurons; (d) computing the weights of said neural network using training examples derived from the at least one group of production rules; (e) expressing said neural network by a polynomial expansion: and (f) computing said at least one network output by substituting said at least one of said plurality of inputs into said polynomial expansion; wherein said one of said system outputs is based on said at least one network output.
Claim 2
Independent Claim
179 words
A method for implementing production rules of an expert system using a neural network, said method comprising the steps of: (a) defining a plurality of inputs and system outputs for said expert system; (b) defining at least one group of production rules which define a relationship between one of said system outputs and at least one of said plurality of inputs; (c) defining said neural network, said neural network having at least one network output, said neural network being responsive to said at least one of said plurality of inputs corresponding to said at least one group of production rules, and said neural network comprising a plurality of neurons; (d) computing the weights of said neural network using training examples derived from the at least one group of production rules; (e) expressing said neural network by a polynomial expansion: and (f) computing said at least one network output by substituting said at least one of said plurality of inputs into said polynomial expansion; wherein said one of said system outputs is based on said at least one network output.
Claim 3
Dependent Claim
24 words
The method recited in claim 1, wherein step (f) includes the substep of: generating said at least one network output as an interpolated value.
Claim 4
Dependent Claim
42 words
The method of claim 2, wherein at least one of said system outputs is quantized into any one of a number of N quantified values, N being a positive integer, and said quantified values having a minimum value and a maximum value.
Claim 5
Dependent Claim
16 words
The method of claim 1, wherein step (a) further comprises the substep of normalizing said inputs.
Claim 6
Dependent Claim
21 words
The method of claim 4, wherein said normalizing substep is performed by transforming the inputs into values between -1 and +1.
Claim 7
Dependent Claim
22 words
The method of claim 4, wherein said normalizing substep is performed by transforming the inputs into absolute values between 0 and 1.
Claim 8
Dependent Claim
28 words
The method of claim 1, wherein in step (a) each input is quantized into any one of a number of N quantified values, N being a positive integer.
Claim 9
Dependent Claim
21 words
The method of claim 1, wherein step (a) further comprises the substep of normalizing at least one of the system outputs.
Claim 10
Dependent Claim
26 words
The method of claim 8, wherein said normalizing substep is performed by transforming said at least one system output into a value between -1 and 1.
Claim 11
Dependent Claim
27 words
The method of claim 8, wherein said normalizing substep is performed by transforming said at least one system output into an absolute value between 0 and 1.
Claim 12
Independent Claim
227 words
A method for implementing an expert system with a plurality of neural networks, said method comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one subsystem output; (b) defining a group of production rules for each of said subsystems which define a relationship between said at least one subsystem output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, each of said neural networks corresponding to a respective subsystem, each of said neural networks responsive to said at least one input of said respective subsystem and having a at least one network output, each of said neural networks comprising a plurality of neurons; (e) computing the weights of said plurality of neural networks using training examples for each of said neural networks derived from the group of production rules for said respective subsystem; and (f) expressing each of said neural networks by a polynomial expansion; and (g) computing said at least one network output from at least one of said neural networks by substituting said at least one input into said polynomial expansion corresponding to said at least one of said neural networks: wherein said at least one subsystem output is based on said at least one network output.
Claim 13
Independent Claim
227 words
A method for implementing an expert system with a plurality of neural networks, said method comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one subsystem output; (b) defining a group of production rules for each of said subsystems which define a relationship between said at least one subsystem output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, each of said neural networks corresponding to a respective subsystem, each of said neural networks responsive to said at least one input of said respective subsystem and having a at least one network output, each of said neural networks comprising a plurality of neurons; (e) computing the weights of said plurality of neural networks using training examples for each of said neural networks derived from the group of production rules for said respective subsystem; and (f) expressing each of said neural networks by a polynomial expansion; and (g) computing said at least one network output from at least one of said neural networks by substituting said at least one input into said polynomial expansion corresponding to said at least one of said neural networks: wherein said at least one subsystem output is based on said at least one network output.
Claim 14
Dependent Claim
24 words
The method recited in claim 11, wherein step (g) includes the substep of: generating said at least one network output as an interpolated value.
Claim 15
Dependent Claim
41 words
The method of claim 12, wherein said at least one subsystem output is quantized into any one of a number of N quantified values, N being a positive integer, and said quantified values having a minimum value and a maximum value.
Claim 16
Dependent Claim
19 words
The method of claim 11, wherein step (a) further comprises the substep of normalizing said at least one input.
Claim 17
Dependent Claim
25 words
The method of claim 14, wherein said normalizing substep is performed by transforming said at least one input into a value between -1 and +1.
Claim 18
Dependent Claim
26 words
The method of claim 14, wherein said normalizing substep is performed by transforming said at least one input into an absolute value between 0 and 1.
Claim 19
Dependent Claim
31 words
The method of claim 11, wherein in step (a) said at least one input is quantized into any one of a number of N quantified values, N being a positive integer.
Claim 20
Dependent Claim
20 words
The method of claim 11, wherein step (a) further comprises the substep of normalizing said at least one subsystem output.
Claim 21
Dependent Claim
26 words
The method of claim 18, wherein said normalizing substep is performed by transforming said at least one subsystem output into a value between -1 and +1.
Claim 22
Dependent Claim
27 words
The method of claim 18, wherein said normalizing substep is performed by transforming said at least one subsystem output into an absolute value between 0 and 1.
Claim 23
Independent Claim
210 words
A method for implementing an expert system with a plurality of neural networks comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one subsystem output; (b) defining a table for each of said subsystems, said table comprising at least one entry defining a relationship between said at least one subsystem output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, each of said neural networks corresponding to a respective subsystem, each of said neural networks responsive to said at least one input of said respective subsystem and having at least one network output, each of said neural networks comprising a plurality of neurons; (e) computing the weights of said neural networks using training examples for each of said neural networks derived from the table for said respective subsystem; (f) expressing each of said neural networks by a polynomial expansion; and (g) computing said at least one network output by substituting said at least one input into said polynomial expansion corresponding to one of said neural networks; wherein said at least one subsystem output is based on said at least one network output.
Claim 24
Independent Claim
210 words
A method for implementing an expert system with a plurality of neural networks comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one subsystem output; (b) defining a table for each of said subsystems, said table comprising at least one entry defining a relationship between said at least one subsystem output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, each of said neural networks corresponding to a respective subsystem, each of said neural networks responsive to said at least one input of said respective subsystem and having at least one network output, each of said neural networks comprising a plurality of neurons; (e) computing the weights of said neural networks using training examples for each of said neural networks derived from the table for said respective subsystem; (f) expressing each of said neural networks by a polynomial expansion; and (g) computing said at least one network output by substituting said at least one input into said polynomial expansion corresponding to one of said neural networks; wherein said at least one subsystem output is based on said at least one network output.
Claim 25
Dependent Claim
24 words
The method recited in claim 21, wherein step (g) includes the substep of: generating said at least one network output as an interpolated value.
Claim 26
Dependent Claim
41 words
The method of claim 22, wherein said at least one subsystem output is quantized into any one of a number of N quantified values, N being a positive integer, and said quantified values having a minimum value and a maximum value.
Claim 27
Dependent Claim
19 words
The method of claim 21, wherein step (a) further comprises the substep of normalizing said at least one input.
Claim 28
Dependent Claim
24 words
The method of claim 24, wherein said normalizing substep is performed by transforming said at least one input into values between -1 and +1.
Claim 29
Dependent Claim
25 words
The method of claim 24, wherein said normalizing substep is performed by transforming said at least one input into absolute values between 0 and 1.
Claim 30
Dependent Claim
31 words
The method of claim 21, wherein in step (a) said at least one input is quantized into any one of a number of N Quantified values, N being a positive integer.
Claim 31
Dependent Claim
20 words
The method of claim 21, wherein step (a) further comprises the substep of normalizing said at least one subsystem output.
Claim 32
Dependent Claim
26 words
The method of claim 28, wherein said normalizing substep is performed by transforming said at least one subsystem output into a value between -1 and +1.
Claim 33
Dependent Claim
27 words
The method of claim 28, wherein said normalizing substep is performed by transforming said at least one subsystem output into an absolute value between 0 and 1.
Claim 34
Independent Claim
200 words
A method for utilizing an expert system implementable with a plurality of neural networks, said method comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one output; (b) defining a group of production rules for each of said subsystems which define a relationship between said at least one output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, one for each corresponding group of production rules, each of said networks comprising a plurality of neurons; (e) computing the weights of said neural networks using training examples for each of said neural networks derived from the corresponding group of production rules; (f) expressing each of said neural networks by a polynomial expansion: (g) distributing a set of input values derived from said at least one input to at least one of said neural networks; and (h) said at least one neural network calculating an output value for said set of input values by substituting said set of input values into said polynomial expansion corresponding to said at least one of said neural networks.
Claim 35
Independent Claim
200 words
A method for utilizing an expert system implementable with a plurality of neural networks, said method comprising the steps of: (a) defining a plurality of subsystems for a problem, each subsystem having at least one input and at least one output; (b) defining a group of production rules for each of said subsystems which define a relationship between said at least one output and said at least one input; (c) linking said subsystems together to form said expert system; (d) defining said plurality of neural networks, one for each corresponding group of production rules, each of said networks comprising a plurality of neurons; (e) computing the weights of said neural networks using training examples for each of said neural networks derived from the corresponding group of production rules; (f) expressing each of said neural networks by a polynomial expansion: (g) distributing a set of input values derived from said at least one input to at least one of said neural networks; and (h) said at least one neural network calculating an output value for said set of input values by substituting said set of input values into said polynomial expansion corresponding to said at least one of said neural networks.
Claim 36
Dependent Claim
21 words
The method recited in claim 31, wherein step (h) includes the substep of: generating said output value as an interpolated value.
Claim 37
Dependent Claim
17 words
The method of claim 31, wherein step (f) further comprises the substep of normalizing said input values.
Claim 38
Dependent Claim
21 words
The method of claim 33, wherein said normalizing substep is performed by transforming said inputs into values between -1 and +1.
Claim 39
Dependent Claim
22 words
The method of claim 33, wherein said normalizing substep is performed by transforming said inputs into absolute values between 0 and 1.
Claim 40
Dependent Claim
31 words
The method of claim 31, wherein in step (a) said at least one input is quantized into any one of a number of N quantified values, N being a positive integer.
Claim 41
Dependent Claim
19 words
The method of claim 31, wherein step (a) further comprises the substep of normalizing said at least one output.
Claim 42
Dependent Claim
25 words
The method of claim 37, wherein said normalizing substep is performed by transforming said at least one output into a value between -1 and +1.
Claim 43
Dependent Claim
26 words
The method of claim 37, wherein said normalizing substep is performed by transforming said at least one output into an absolute value between 0 and 1.
Claim 44
Dependent Claim
38 words
The method of claim 31, wherein said output value is quantized into any one of a number of N quantified values, N being a positive integer, and said quantified values having a minimum value and a maximum value.
References Cited (Backward Citations)
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