US5828812A
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Recurrent neural network-based fuzzy logic system and method
Assignee: National Semiconductor Corp
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Filing: 1995-05-02
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Granted: 1998-10-27
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Expires: 2015-05-02
Abstract
Abstract A recurrent, neural network-based fuzzy logic system includes in a rule base layer and a membership function layer neurons which each have a recurrent architecture with an output-to-input feedback path including a time delay element and a neural weight. Further included is a recurrent, neural network-based fuzzy logic rule generator wherein a neural network receives and fuzzifies input data and provides data corresponding to fuzzy logic membership functions and recurrent fuzzy logic rules.
Inventors
- 👤 Emdadur Rahman Khan
- 👤 William Shields Neely
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-07 14:46
CPC Classifications
G; G06; G06N; G06N7/00; G06N7/02; G06N7/04; G06N7/046; G06N3/00; G06N3/02; G06N3/04; G06N3/043; Y; Y10; Y10S; Y10S706/00; Y10S706/90
Claims Analysis
Total Claims: 39
Claim 1
Independent Claim
198 words
A recurrent neural network-based fuzzy logic apparatus comprising: first neural means for receiving and processing first and second pluralities of input signals and in accordance therewith providing first and second pluralities of neural signals, wherein said first plurality of input signals originates externally to said first neural means and said first plurality of neural signals corresponds to a first plurality of fuzzy logic rule antecedents; first time-delay means for intra-neurally receiving and time-delaying said second plurality of neural signals and in accordance therewith providing a first plurality of intra-neural recurrent signals as said second plurality of input signals; second neural means for receiving and processing said first plurality of neural signals from said first neural means and a plurality of antecedent signals and in accordance therewith providing a plurality of consequent signals, wherein said plurality of antecedent signals corresponds to a second plurality of fuzzy logic rule antecedents and said plurality of consequent signals corresponds to a plurality of fuzzy logic rule consequents; and second time-delay means for intra-neurally receiving and time-delaying a portion of said plurality of consequent signals and in accordance therewith providing a second plurality of intra-neural recurrent signals as said plurality of antecedent signals.
Claim 2
Dependent Claim
198 words
A recurrent neural network-based fuzzy logic apparatus comprising: first neural means for receiving and processing first and second pluralities of input signals and in accordance therewith providing first and second pluralities of neural signals, wherein said first plurality of input signals originates externally to said first neural means and said first plurality of neural signals corresponds to a first plurality of fuzzy logic rule antecedents; first time-delay means for intra-neurally receiving and time-delaying said second plurality of neural signals and in accordance therewith providing a first plurality of intra-neural recurrent signals as said second plurality of input signals; second neural means for receiving and processing said first plurality of neural signals from said first neural means and a plurality of antecedent signals and in accordance therewith providing a plurality of consequent signals, wherein said plurality of antecedent signals corresponds to a second plurality of fuzzy logic rule antecedents and said plurality of consequent signals corresponds to a plurality of fuzzy logic rule consequents; and second time-delay means for intra-neurally receiving and time-delaying a portion of said plurality of consequent signals and in accordance therewith providing a second plurality of intra-neural recurrent signals as said plurality of antecedent signals.
Claim 3
Dependent Claim
39 words
The apparatus of claim 1, wherein said first neural means comprises a plurality of neurons for fuzzifying selected ones of said first and second pluralities of input signals and in accordance therewith providing said first plurality of neural signals.
Claim 4
Dependent Claim
16 words
The apparatus of claim 1, wherein said first neural means comprises a plurality of product-based neurons.
Claim 5
Dependent Claim
16 words
The apparatus of claim 1, wherein said first neural means comprises a plurality of sum-based neurons.
Claim 6
Dependent Claim
54 words
The apparatus of claim 1, wherein said first time-delay means comprises a plurality of time-delay elements, each for delaying by at least one unit time delay and selectively weighting a respective one of said second plurality of neural signals and in accordance therewith providing a respective one of said second plurality of input signals.
Claim 7
Dependent Claim
16 words
The apparatus of claim 1, wherein said second neural means comprises a plurality of product-based neurons.
Claim 8
Dependent Claim
16 words
The apparatus of claim 1, wherein said second neural means comprises a plurality of sum-based neurons.
Claim 9
Dependent Claim
55 words
The apparatus of claim 1, wherein said second time-delay means comprises a plurality of time-delay elements, each for delaying by at least one unit time delay and selectively weighting a respective one of said portion of said plurality of consequent signals and in accordance therewith providing a respective one of said plurality of antecedent signals.
Claim 10
Dependent Claim
175 words
A neural network apparatus for generating signals corresponding to a plurality of recurrent fuzzy logic rules, comprising: a first plurality of neurons for receiving and selectively fuzzifying a plurality of input signals and in accordance therewith providing a plurality of fuzzified signals; a second plurality of neurons, coupled to said first plurality of neurons, for receiving said plurality of fuzzified signals therefrom and a first plurality of intra-neural recurrent signals and in accordance therewith providing a plurality of membership signals corresponding to a plurality of fuzzy logic membership functions; a first plurality of time-delay elements, coupled to said second plurality of neurons, for intra-neurally receiving and time-delaying a portion of said plurality of membership signals and in accordance therewith providing said first plurality of intra-neural recurrent signals; and a third plurality of neurons, coupled to said second plurality of neurons, for receiving said plurality of membership signals therefrom and a second plurality of intra-neural recurrent signals and in accordance therewith providing a plurality of rule signals corresponding to a plurality of recurrent fuzzy logic rules.
Claim 11
Dependent Claim
175 words
A neural network apparatus for generating signals corresponding to a plurality of recurrent fuzzy logic rules, comprising: a first plurality of neurons for receiving and selectively fuzzifying a plurality of input signals and in accordance therewith providing a plurality of fuzzified signals; a second plurality of neurons, coupled to said first plurality of neurons, for receiving said plurality of fuzzified signals therefrom and a first plurality of intra-neural recurrent signals and in accordance therewith providing a plurality of membership signals corresponding to a plurality of fuzzy logic membership functions; a first plurality of time-delay elements, coupled to said second plurality of neurons, for intra-neurally receiving and time-delaying a portion of said plurality of membership signals and in accordance therewith providing said first plurality of intra-neural recurrent signals; and a third plurality of neurons, coupled to said second plurality of neurons, for receiving said plurality of membership signals therefrom and a second plurality of intra-neural recurrent signals and in accordance therewith providing a plurality of rule signals corresponding to a plurality of recurrent fuzzy logic rules.
Claim 12
Dependent Claim
43 words
The apparatus of claim 9, further comprising a second plurality of time-delay elements, coupled to said third plurality of neurons, for receiving and time-delaying a portion of said plurality of rule signals and in accordance therewith providing said second plurality of recurrent signals.
Claim 13
Dependent Claim
34 words
The apparatus of claim 9, wherein said third plurality of neurons is further for providing a defuzzified output signal in accordance with said plurality of membership signals and said second plurality of recurrent signals.
Claim 14
Dependent Claim
41 words
The apparatus of claim 11, further comprising a comparator, coupled to said third plurality of neurons, for receiving and comparing a reference signal and said defuzzified output signal and in accordance therewith providing an error signal corresponding to a difference therebetween.
Claim 15
Dependent Claim
36 words
The apparatus of claim 9, further comprising an output neuron layer, coupled to said third plurality of neurons, for receiving and summing a plurality of signals therefrom and in accordance therewith providing a defuzzified output signal.
Claim 16
Dependent Claim
40 words
The apparatus of claim 13, further comprising a comparator, coupled to said output neuron layer, for receiving and comparing a reference signal and said defuzzified output signal and in accordance therewith providing an error signal corresponding to a difference therebetween.
Claim 17
Dependent Claim
150 words
A neural network apparatus for generating a plurality of recurrent fuzzy logic rules, comprising: first neural means for receiving and selectively fuzzifying a plurality of input data and in accordance therewith providing a plurality of fuzzified data; second neural means for receiving said plurality of fuzzified data from said first neural means and a first plurality of intra-neural recurrent data and in accordance therewith providing a plurality of membership data corresponding to a plurality of fuzzy logic membership functions; first time-delay means for intra-neurally receiving and time-delaying a portion of said plurality of membership data and in accordance therewith providing said first plurality of intra-neural recurrent data; and third neural means for receiving said plurality of membership data from said second neural means and a second plurality of intra-neural recurrent data and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules.
Claim 18
Dependent Claim
150 words
A neural network apparatus for generating a plurality of recurrent fuzzy logic rules, comprising: first neural means for receiving and selectively fuzzifying a plurality of input data and in accordance therewith providing a plurality of fuzzified data; second neural means for receiving said plurality of fuzzified data from said first neural means and a first plurality of intra-neural recurrent data and in accordance therewith providing a plurality of membership data corresponding to a plurality of fuzzy logic membership functions; first time-delay means for intra-neurally receiving and time-delaying a portion of said plurality of membership data and in accordance therewith providing said first plurality of intra-neural recurrent data; and third neural means for receiving said plurality of membership data from said second neural means and a second plurality of intra-neural recurrent data and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules.
Claim 19
Dependent Claim
33 words
The apparatus of claim 15, further comprising second time-delay means for receiving and time-delaying a portion of said plurality of rule data and in accordance therewith providing said second plurality of recurrent data.
Claim 20
Dependent Claim
32 words
The apparatus of claim 15, wherein said third neural means is further for providing defuzzified output data in accordance with said plurality of membership data and said second plurality of recurrent data.
Claim 21
Dependent Claim
32 words
The apparatus of claim 17, further comprising comparison means for receiving and comparing reference data and said defuzzified output data and in accordance therewith providing error data corresponding to a difference therebetween.
Claim 22
Dependent Claim
31 words
The apparatus of claim 15, further comprising output neural means for receiving and summing a plurality of data from said third neural means and in accordance therewith providing defuzzified output data.
Claim 23
Dependent Claim
32 words
The apparatus of claim 19, further comprising comparison means for receiving and comparing reference data and said defuzzified output data and in accordance therewith providing error data corresponding to a difference therebetween.
Claim 24
Independent Claim
201 words
A method of signal processing in accordance with recurrent neural network-based fuzzy logic, said method comprising the steps of: receiving and processing first and second pluralities of input signals with a first artificial neural apparatus and in accordance therewith providing first and second pluralities of neural signals, wherein said first plurality of input signals originates externally to said first artificial neural apparatus and said first plurality of neural signals corresponds to a first plurality of fuzzy logic rule antecedents; intra-neurally time-delaying said second plurality of neural signals and in accordance therewith providing a first plurality of intra-neural recurrent signals as said second plurality of input signals; processing said first plurality of neural signals from said first artificial neural apparatus and a plurality of antecedent signals with a second artificial neural apparatus and in accordance therewith providing a plurality of consequent signals, wherein said plurality of antecedent signals corresponds to a second plurality of fuzzy logic rule antecedents and said plurality of consequent signals corresponds to a plurality of fuzzy logic rule consequents; and intra-neurally time-delaying a portion of said plurality of consequent signals and in accordance therewith providing a second plurality of intra-neural recurrent signals as said plurality of antecedent signals.
Claim 25
Independent Claim
201 words
A method of signal processing in accordance with recurrent neural network-based fuzzy logic, said method comprising the steps of: receiving and processing first and second pluralities of input signals with a first artificial neural apparatus and in accordance therewith providing first and second pluralities of neural signals, wherein said first plurality of input signals originates externally to said first artificial neural apparatus and said first plurality of neural signals corresponds to a first plurality of fuzzy logic rule antecedents; intra-neurally time-delaying said second plurality of neural signals and in accordance therewith providing a first plurality of intra-neural recurrent signals as said second plurality of input signals; processing said first plurality of neural signals from said first artificial neural apparatus and a plurality of antecedent signals with a second artificial neural apparatus and in accordance therewith providing a plurality of consequent signals, wherein said plurality of antecedent signals corresponds to a second plurality of fuzzy logic rule antecedents and said plurality of consequent signals corresponds to a plurality of fuzzy logic rule consequents; and intra-neurally time-delaying a portion of said plurality of consequent signals and in accordance therewith providing a second plurality of intra-neural recurrent signals as said plurality of antecedent signals.
Claim 26
Dependent Claim
67 words
The method of claim 21, wherein said step of receiving and processing first and second pluralities of input signals with a first artificial neural apparatus and in accordance therewith providing first and second pluralities of neural signals comprises fuzzifying selected ones of said first and second pluralities of input signals with a plurality of artificial neurons and in accordance therewith providing said first plurality of neural signals.
Claim 27
Dependent Claim
54 words
The method of claim 21, wherein said step of receiving and processing first and second pluralities of input signals with a first artificial neural apparatus and in accordance therewith providing first and second pluralities of neural signals comprises processing said first and second pluralities of input signals with a plurality of product-based artificial neurons.
Claim 28
Dependent Claim
54 words
The method of claim 21, wherein said step of receiving and processing first and second pluralities of input signals with a first artificial neural apparatus and in accordance therewith providing first and second pluralities of neural signals comprises processing said first and second pluralities of input signals with a plurality of sum-based artificial neurons.
Claim 29
Dependent Claim
48 words
The method of claim 21, wherein said step of time-delaying said second plurality of neural signals and in accordance therewith providing said second plurality of input signals comprises delaying by at least one unit time delay and selectively weighting individual ones of said second plurality of neural signals.
Claim 30
Dependent Claim
65 words
The method of claim 21, wherein said step of processing said first plurality of neural signals and a plurality of antecedent signals with a second artificial neural apparatus and in accordance therewith providing a plurality of output signals and a plurality of consequent signals comprises processing said first plurality of neural signals and a plurality of antecedent signals with a plurality of product-based artificial neurons.
Claim 31
Dependent Claim
65 words
The method of claim 21, wherein said step of processing said first plurality of neural signals and a plurality of antecedent signals with a second artificial neural apparatus and in accordance therewith providing a plurality of output signals and a plurality of consequent signals comprises processing said first plurality of neural signals and a plurality of antecedent signals with a plurality of sum-based artificial neurons.
Claim 32
Dependent Claim
48 words
The method of claim 21, wherein said step of time-delaying a portion of said plurality of consequent signals and in accordance therewith providing said plurality of antecedent signals comprises delaying by at least one unit time delay and selectively weighting individual ones of said plurality of consequent signals.
Claim 33
Independent Claim
150 words
A method of generating a plurality of recurrent fuzzy logic rules, comprising: receiving and selectively fuzzifying a plurality of input data with a first artificial neural apparatus and in accordance therewith providing a plurality of fuzzified data; receiving said plurality of fuzzified data from said first artificial neural apparatus and a first plurality of intra-neural recurrent data with a second artificial neural apparatus and in accordance therewith providing a plurality of membership data corresponding to a plurality of fuzzy logic membership functions; intra-neurally time-delaying a portion of said plurality of membership data and in accordance therewith providing said first plurality of intra-neural recurrent data; and receiving said plurality of membership data from said second artificial neural apparatus and a second plurality of intra-neural recurrent data with a third artificial neural apparatus and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules.
Claim 34
Independent Claim
150 words
A method of generating a plurality of recurrent fuzzy logic rules, comprising: receiving and selectively fuzzifying a plurality of input data with a first artificial neural apparatus and in accordance therewith providing a plurality of fuzzified data; receiving said plurality of fuzzified data from said first artificial neural apparatus and a first plurality of intra-neural recurrent data with a second artificial neural apparatus and in accordance therewith providing a plurality of membership data corresponding to a plurality of fuzzy logic membership functions; intra-neurally time-delaying a portion of said plurality of membership data and in accordance therewith providing said first plurality of intra-neural recurrent data; and receiving said plurality of membership data from said second artificial neural apparatus and a second plurality of intra-neural recurrent data with a third artificial neural apparatus and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules.
Claim 35
Dependent Claim
69 words
The method of claim 29, wherein said step of receiving said plurality of membership data and a second plurality of recurrent data with a third artificial neural apparatus and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules further comprises time-delaying a portion of said plurality of rule data and in accordance therewith providing said second plurality of recurrent data.
Claim 36
Dependent Claim
68 words
The method of claim 29, wherein said step of receiving said plurality of membership data and a second plurality of recurrent data with a third artificial neural apparatus and in accordance therewith providing a plurality of rule data corresponding to a plurality of recurrent fuzzy logic rules further comprises providing defuzzified output data in accordance with said plurality of membership data and said second plurality of recurrent data.
Claim 37
Dependent Claim
32 words
The method of claim 31, further comprising the step of receiving and comparing reference data and said defuzzified output data and in accordance therewith providing error data corresponding to a difference therebetween.
Claim 38
Dependent Claim
31 words
The method of claim 29, further comprising the step of receiving and summing a plurality of data from said third artificial neural apparatus and in accordance therewith providing defuzzified output data.
Claim 39
Dependent Claim
32 words
The method of claim 33, further comprising the step of receiving and comparing reference data and said defuzzified output data and in accordance therewith providing error data corresponding to a difference therebetween.
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