US5579439A
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US
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Fuzzy logic design generator using a neural network to generate fuzzy logic rules and membership functions for use in intelligent systems
Assignee: National Semiconductor Corp
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Filing: 1993-03-24
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Granted: 1996-11-26
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Expires: 2013-03-24
Abstract
Abstract A fuzzy logic design generator for providing a fuzzy logic design for an intelligent controller in a plant control system includes an artificial neural network for generating fuzzy logic rules and membership functions data. These fuzzy logic rules and membership functions data can be stored for use in a fuzzy logic system for neural network based fuzzy antecedent processing, rule evaluation and defuzzification, thereby avoiding heuristics associated with conventional fuzzy logic algorithms. The neural network, used as a fuzzy rule generator to generate fuzzy logic rules and membership functions for the system's plant controller, is a multilayered feed-forward neural network based upon a modified version of a back-propagation neural network and learns the system behavior in accordance with input and output data and then maps the acquired knowledge into a new non-heuristic fuzzy logic system. Interlayer weights of the neural network are mapped into fuzzy logic rules and membership functions. Antecedent processing is performed according to a weighted product of the antecedents. One layer of the neural network is used for performing rule evaluation and defuzzification.
Inventors
- 👤 Emdadur R. Khan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-01 14:09
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: 99
Claim 1
Independent Claim
224 words
An artificial neural network for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising: first neural means for receiving a plurality of signals representing input data for an intelligent system and for providing fuzzified data which corresponds to said input data; second neural means coupled to said first neural means for receiving said fuzzified data and in accordance therewith generating a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; and third neural means coupled to said second neural means for receiving said plurality of membership signals and in accordance therewith generating a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules and in accordance therewith generating an output signal which represents defuzzified data, wherein said third neural means includes a single output neuron for receiving and processing said plurality of intermediate signals and in accordance therewith generating said output signal; wherein said first, second and third neural means cooperate together by performing a learning process with back-propagation of an output error signal based upon said output signal, and wherein said output error signal is propagated serially back from an output of said third neural means through and successively processed by said third, second and first neural means.
Claim 2
Dependent Claim
224 words
An artificial neural network for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising: first neural means for receiving a plurality of signals representing input data for an intelligent system and for providing fuzzified data which corresponds to said input data; second neural means coupled to said first neural means for receiving said fuzzified data and in accordance therewith generating a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; and third neural means coupled to said second neural means for receiving said plurality of membership signals and in accordance therewith generating a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules and in accordance therewith generating an output signal which represents defuzzified data, wherein said third neural means includes a single output neuron for receiving and processing said plurality of intermediate signals and in accordance therewith generating said output signal; wherein said first, second and third neural means cooperate together by performing a learning process with back-propagation of an output error signal based upon said output signal, and wherein said output error signal is propagated serially back from an output of said third neural means through and successively processed by said third, second and first neural means.
Claim 3
Dependent Claim
31 words
An artificial neural network as recited in claim 1, wherein said first neural means comprises a first plurality of artificial neurons for fuzzifying said input data to produce said fuzzified data.
Claim 4
Dependent Claim
35 words
An artificial neural network as recited in claim 1, wherein said second neural means comprises a second plurality of artificial neurons for generating a plurality of neuron output signals as said plurality of membership signals.
Claim 5
Dependent Claim
24 words
An artificial neural network as recited in claim 1, wherein said third neural means multiplies selected ones of said received plurality of membership signals.
Claim 6
Dependent Claim
36 words
An artificial neural network as recited in claim 1, wherein said third neural means comprises a third plurality of artificial neurons which includes a plurality of output weights corresponding to said plurality of logic rule signals.
Claim 7
Dependent Claim
26 words
An artificial neural network as recited in claim 5, wherein said third plurality of artificial neurons multiply selected ones of said received plurality of membership signals.
Claim 8
Dependent Claim
63 words
An artificial neural network as recited in claim 1, further comprising comparison means coupled to said third neural means for receiving therefrom an output signal representing a plant control signal, for receiving an input signal representing a desired control signal, and for comparing said input desired control signal with said output plant control signal and in accordance therewith generating said output error signal.
Claim 9
Dependent Claim
16 words
An artificial neural network as recited in claim 7, wherein said comparison means comprises a comparator.
Claim 10
Dependent Claim
37 words
An artificial neural network as recited in claim 1, further comprising fourth neural means coupled to said third neural..means for receiving and summing a plurality of signals therefrom to provide an output sum signal representing defuzzified data.
Claim 11
Dependent Claim
18 words
An artificial neural network as recited in claim 9, wherein said fourth neural means comprises an artificial neuron.
Claim 12
Dependent Claim
68 words
An artificial neural network as recited in claim 9, further comprising comparison means coupled to said fourth neural means for receiving therefrom said output sum signal, for receiving an input signal representing a desired control signal, and for comparing said input desired control signal with said output sum signal and in accordance therewith generating said output error signal, wherein said output sum signal represents a plant control signal.
Claim 13
Dependent Claim
16 words
An artificial neural network as recited in claim 11, wherein said comparison means comprises a comparator.
Claim 14
Dependent Claim
258 words
An artificial neural network for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising: a first plurality of artificial neurons which receive a plurality of signals representing input data for an intelligent system and provide fuzzified data which correspond to said input data; a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive said fuzzified data and in accordance therewith generate a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; and a third plurality of artificial neurons, coupled to said second plurality of artificial neurons, which receive said plurality of membership signals and in accordance therewith generate a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules and in accordance therewith generate an output signal which represents defuzzified data, wherein said third plurality of artificial neurons includes a plurality of output weights corresponding to said plurality of logic rule signals and a single output neuron which receives and processes said plurality of intermediate signals and in accordance therewith generates said output signal; wherein said first, second and third pluralities of artificial neurons cooperate together by performing a learning process with back-propagation of an output error signal based upon said output signal, and wherein said output error signal is propagated serially back from an output of said third plurality of artificial neurons through and successively processed by said third, second and first pluralities of artificial neurons.
Claim 15
Dependent Claim
258 words
An artificial neural network for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising: a first plurality of artificial neurons which receive a plurality of signals representing input data for an intelligent system and provide fuzzified data which correspond to said input data; a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive said fuzzified data and in accordance therewith generate a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; and a third plurality of artificial neurons, coupled to said second plurality of artificial neurons, which receive said plurality of membership signals and in accordance therewith generate a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules and in accordance therewith generate an output signal which represents defuzzified data, wherein said third plurality of artificial neurons includes a plurality of output weights corresponding to said plurality of logic rule signals and a single output neuron which receives and processes said plurality of intermediate signals and in accordance therewith generates said output signal; wherein said first, second and third pluralities of artificial neurons cooperate together by performing a learning process with back-propagation of an output error signal based upon said output signal, and wherein said output error signal is propagated serially back from an output of said third plurality of artificial neurons through and successively processed by said third, second and first pluralities of artificial neurons.
Claim 16
Dependent Claim
26 words
An artificial neural network as recited in claim 13, wherein said third plurality of artificial neurons multiply selected ones of said received plurality of membership signals.
Claim 17
Dependent Claim
63 words
An artificial neural network as recited in claim 13, further comprising a comparator, coupled to said third plurality of artificial neurons, which receives therefrom an output signal representing a plant control signal, receives an input signal representing a desired control signal, and compares said input desired control signal with said output plant control signal and in accordance therewith generates said output error signal.
Claim 18
Dependent Claim
40 words
An artificial neural network as recited in claim 13, further comprising an artificial neuron, coupled to said third plurality of artificial neurons, which receives and sums a plurality of signals therefrom to provide an output sum signal representing defuzzified data.
Claim 19
Dependent Claim
65 words
An artificial neural network as recited in claim 16, further comprising a comparator, coupled to said artificial neuron, which receives therefrom said output sum signal, receives an input signal representing a desired control signal, and compares said input desired control signal with said output sum signal and in accordance therewith generates said output error signal, wherein said output sum signal represents a plant control signal.
Claim 20
Dependent Claim
222 words
A computer-implemented method for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising the computer-implemented steps of: receiving a plurality of signals representing input data for an intelligent system; generating a plurality of fuzzified data signals representing fuzzified data which corresponds to said input data; generating in accordance with said plurality of fuzzified data signals a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; generating in accordance with said plurality of membership signals a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules; processing said plurality of intermediate signals with a single output neuron and in accordance therewith generating an output signal which represents defuzzified data; and performing a learning process with back-propagation of an output error signal based upon said output signal in cooperation with said steps of generating said pluralities of fuzzified data signals, membership signals and logic rule signals, wherein said back-propagation of said output error signal is performed following a completion of one iteration of said step of generating said plurality of logic rule signals and is performed successively and prior to subsequent iterations of said steps of generating said pluralities of fuzzified data signals, membership signals and logic rule signals.
Claim 21
Dependent Claim
222 words
A computer-implemented method for generating pluralities of signals representing a plurality of fuzzy logic rules and a plurality of fuzzy logic membership functions, comprising the computer-implemented steps of: receiving a plurality of signals representing input data for an intelligent system; generating a plurality of fuzzified data signals representing fuzzified data which corresponds to said input data; generating in accordance with said plurality of fuzzified data signals a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions; generating in accordance with said plurality of membership signals a plurality of intermediate signals and a plurality of logic rule signals which represent a plurality of fuzzy logic rules; processing said plurality of intermediate signals with a single output neuron and in accordance therewith generating an output signal which represents defuzzified data; and performing a learning process with back-propagation of an output error signal based upon said output signal in cooperation with said steps of generating said pluralities of fuzzified data signals, membership signals and logic rule signals, wherein said back-propagation of said output error signal is performed following a completion of one iteration of said step of generating said plurality of logic rule signals and is performed successively and prior to subsequent iterations of said steps of generating said pluralities of fuzzified data signals, membership signals and logic rule signals.
Claim 22
Dependent Claim
47 words
A method as recited in claim 18, wherein said step of generating a plurality of fuzzified data signals representing fuzzified data which corresponds to said input data comprises fuzzifying said input data with a first plurality of artificial neurons to produce said plurality of fuzzified data signals.
Claim 23
Dependent Claim
66 words
A method as recited in claim 18, wherein said step of generating in accordance with said plurality of fuzzified data signals a plurality of membership signals which correspond to a plurality of fuzzy logic membership functions comprises inputting said plurality of fuzzified data signals to a second plurality of artificial neurons and outputting therefrom a plurality of neuron output signals as said plurality of membership signals.
Claim 24
Dependent Claim
69 words
A method as recited in claim 18, wherein said step of generating in accordance with said plurality of membership signals a plurality of logic rule signals which represent a plurality of fuzzy logic rules comprises outputting a plurality of weight signals corresponding to a plurality of output weights associated with a third plurality of artificial neurons, wherein said plurality of weight signals represent said plurality of logic rule signals.
Claim 25
Dependent Claim
46 words
A method as recited in claim 21, wherein said step of generating in accordance with said plurality of membership signals a plurality of logic rule signals which represent a plurality of fuzzy logic rules further comprises multiplying selected ones of said received plurality of membership signals.
Claim 26
Dependent Claim
45 words
A method as recited in claim 18, wherein said step of generating in accordance with said plurality of membership signals a plurality of logic rule signals which represent a plurality of fuzzy logic rules comprises multiplying selected ones of said received plurality of membership signals.
Claim 27
Dependent Claim
52 words
A method as recited in claim 18, further comprising the steps of: providing an output signal representing a plant control signal; receiving an input signal representing a desired control signal; and comparing said input desired control signal with said output plant control signal and in accordance therewith generating said output error signal.
Claim 28
Dependent Claim
50 words
A method as recited in claim 18, further comprising the steps of: generating a plurality of output signals associated with said plurality of logic rule signals; and summing said plurality of output signals associated with said plurality of logic rule signals to provide an output sum signal representing defuzzified data.
Claim 29
Dependent Claim
45 words
A method as recited in claim 25, wherein said step of summing said plurality of output signals associated with said plurality of logic rule signals to provide an output sum signal representing defuzzified data comprises summing said plurality of output signals with an artificial neuron.
Claim 30
Dependent Claim
57 words
A method as recited in claim 25, further comprising the steps of: receiving said output sum signal, wherein said output sum signal represents a plant control signal; receiving an input signal representing a desired control signal; and comparing said input desired control signal with said output sum signal and in accordance therewith generating said output error signal.
Claim 31
Dependent Claim
114 words
An artificial neural network for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising: rule-based neural means for receiving and processing a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions to provide in accordance therewith a plurality of processed fuzzy signals; output weights means coupled to said rule-based neural means for receiving and weighting said plurality of processed fuzzy signals; and single output neuron means coupled to said output weights means for receiving and summing said plurality of weighted, processed fuzzy signals to provide an output signal representing defuzzified data.
Claim 32
Dependent Claim
114 words
An artificial neural network for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising: rule-based neural means for receiving and processing a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions to provide in accordance therewith a plurality of processed fuzzy signals; output weights means coupled to said rule-based neural means for receiving and weighting said plurality of processed fuzzy signals; and single output neuron means coupled to said output weights means for receiving and summing said plurality of weighted, processed fuzzy signals to provide an output signal representing defuzzified data.
Claim 33
Dependent Claim
23 words
An artificial neural network as recited in claim 28, wherein said rule-based neural means multiplies selected ones of said plurality of fuzzy signals.
Claim 34
Dependent Claim
21 words
An artificial neural network as recited in claim 28, wherein said rule-based neural means comprises a first plurality of artificial neurons.
Claim 35
Dependent Claim
25 words
An artificial neural network as recited in claim 30, wherein said first plurality of artificial neurons multiply selected ones of said plurality of fuzzy signals.
Claim 36
Dependent Claim
24 words
An artificial neural network as recited in claim 28, wherein said plurality of fuzzy signals corresponds to a plurality of fuzzy logic membership functions.
Claim 37
Dependent Claim
20 words
An artificial neural network as recited in claim 28, wherein said output weights means comprises a plurality of interneuron weights.
Claim 38
Dependent Claim
23 words
An artificial neural network as recited in claim 33, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 39
Dependent Claim
20 words
An artificial neural network as recited in claim 28, wherein said single output neuron means comprises a single artificial neuron.
Claim 40
Dependent Claim
37 words
An artificial neural network as recited in claim 28, further comprising fuzzifier neural means coupled to said rule-based neural means for receiving and fuzzifying a plurality of data signals to provide said plurality of fuzzy signals thereto.
Claim 41
Dependent Claim
21 words
An artificial neural network as recited in claim 36, wherein said fuzzifier neural means comprises a second plurality of artificial neurons.
Claim 42
Dependent Claim
122 words
An artificial neural network for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising: a first plurality of artificial neurons which receive and process a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions to provide in accordance therewith a plurality of processed fuzzy signals; a plurality of interneuron weights, coupled to said first plurality of artificial neurons, which receive and weight said plurality of processed fuzzy signals; and a single artificial neuron, coupled to said plurality of interneuron weights, which receives and sums said plurality of weighted, processed fuzzy signals to provide an output signal representing defuzzified data.
Claim 43
Dependent Claim
122 words
An artificial neural network for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising: a first plurality of artificial neurons which receive and process a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions to provide in accordance therewith a plurality of processed fuzzy signals; a plurality of interneuron weights, coupled to said first plurality of artificial neurons, which receive and weight said plurality of processed fuzzy signals; and a single artificial neuron, coupled to said plurality of interneuron weights, which receives and sums said plurality of weighted, processed fuzzy signals to provide an output signal representing defuzzified data.
Claim 44
Dependent Claim
25 words
An artificial neural network as recited in claim 38, wherein said first plurality of artificial neurons multiply selected ones of said plurality of fuzzy signals.
Claim 45
Dependent Claim
24 words
An artificial neural network as recited in claim 38, wherein said plurality of fuzzy signals corresponds to a plurality of fuzzy logic membership functions.
Claim 46
Dependent Claim
23 words
An artificial neural network as recited in claim 38, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 47
Dependent Claim
42 words
An artificial neural network as recited in claim 38, further comprising a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive and fuzzify a plurality of data signals to provide said plurality of fuzzy signals thereto.
Claim 48
Dependent Claim
96 words
A computer-implemented method for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising the computer-implemented steps of: receiving a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions; processing said plurality of fuzzy signals to provide a plurality of processed fuzzy signals; weighting said plurality of processed fuzzy signals; and summing said plurality of weighted, processed fuzzy signals, in accordance with a single summing function, to provide an output signal representing defuzzified data.
Claim 49
Dependent Claim
96 words
A computer-implemented method for defuzzifying a plurality of fuzzy data generated in accordance with a plurality of fuzzy logic rules and membership functions, comprising the computer-implemented steps of: receiving a plurality of fuzzy signals representing fuzzified data which has been fuzzified in accordance with a plurality of fuzzy logic membership functions; processing said plurality of fuzzy signals to provide a plurality of processed fuzzy signals; weighting said plurality of processed fuzzy signals; and summing said plurality of weighted, processed fuzzy signals, in accordance with a single summing function, to provide an output signal representing defuzzified data.
Claim 50
Dependent Claim
35 words
A method as recited in claim 43, wherein said step of processing said plurality of fuzzy signals to provide a plurality of processed fuzzy signals comprises multiplying selected ones of said plurality of fuzzy signals.
Claim 51
Dependent Claim
38 words
A method as recited in claim 43, wherein said step of processing said plurality of fuzzy signals to provide a plurality of processed fuzzy signals comprises processing said plurality of fuzzy signals with a plurality of artificial neurons.
Claim 52
Dependent Claim
36 words
A method as recited in claim 45, wherein said step of processing said plurality of fuzzy signals to provide a plurality of processed fuzzy signals further comprises multiplying selected ones of said plurality of fuzzy signals.
Claim 53
Dependent Claim
22 words
A method as recited in claim 43, wherein said plurality of fuzzy signals corresponds to a plurality of fuzzy logic membership functions.
Claim 54
Dependent Claim
32 words
A method as recited in claim 43, wherein said step of weighting said plurality of processed fuzzy signals comprises weighting said plurality of processed fuzzy signals with a plurality of interneuron weights.
Claim 55
Dependent Claim
21 words
A method as recited in claim 48, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 56
Dependent Claim
38 words
A method as recited in claim 43, wherein said step of summing said plurality of weighted, processed fuzzy signals to provide a defuzzified output signal comprises summing said plurality of weighted, processed fuzzy signals with an artificial neuron.
Claim 57
Dependent Claim
27 words
A method as recited in claim 43, further comprising the step of receiving and fuzzifying a plurality of data signals to provide said plurality of fuzzy signals.
Claim 58
Dependent Claim
42 words
A method as recited in claim 51, wherein said step of receiving and fuzzifying a plurality of data signals to provide said plurality of fuzzy signals comprises receiving and fuzzifying said plurality of data signals with a second plurality of artificial neurons.
Claim 59
Dependent Claim
90 words
An artificial neural network for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising: input neural means for receiving and fuzzifying a plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and output neural means coupled to said input neural means for receiving and multiplying together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 60
Dependent Claim
90 words
An artificial neural network for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising: input neural means for receiving and fuzzifying a plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and output neural means coupled to said input neural means for receiving and multiplying together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 61
Dependent Claim
21 words
An artificial neural network as recited in claim 53, wherein said input neural means comprises a first plurality of artificial neurons.
Claim 62
Dependent Claim
21 words
An artificial neural network as recited in claim 53, wherein said output neural means comprises a second plurality of artificial neurons.
Claim 63
Dependent Claim
37 words
An artificial neural network as recited in claim 53, further comprising output weights means coupled to said output neural means for receiving and weighting said plurality of product signals to provide a plurality of weighted product signals.
Claim 64
Dependent Claim
20 words
An artificial neural network as recited in claim 56, wherein said output weights means comprises a plurality of interneuron weights.
Claim 65
Dependent Claim
23 words
An artificial neural network as recited in claim 57, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 66
Dependent Claim
30 words
An artificial neural network as recited in claim 56, further comprising neural summing means coupled to said output weights means for receiving and summing said plurality of weighted product signals.
Claim 67
Dependent Claim
18 words
An artificial neural network as recited in claim 59, wherein said neural summing means comprises an artificial neuron.
Claim 68
Dependent Claim
98 words
An artificial neural network for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising: a first plurality of artificial neurons which receive and fuzzify a plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive and multiply together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 69
Dependent Claim
98 words
An artificial neural network for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising: a first plurality of artificial neurons which receive and fuzzify a plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive and multiply together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 70
Dependent Claim
41 words
An artificial neural network as recited in claim 61, further comprising a plurality of interneuron weights, coupled to said second plurality of artificial neurons, which receive and weight said plurality of product signals to provide a plurality of weighted product signals.
Claim 71
Dependent Claim
23 words
An artificial neural network as recited in claim 62, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 72
Dependent Claim
31 words
An artificial neural network as recited in claim 62, further comprising an artificial neuron, coupled to said plurality of interneuron weights, which receives and sums said plurality of weighted product signals.
Claim 73
Dependent Claim
82 words
A computer-implemented method for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising the computer-implemented steps of: receiving a plurality of input data signals; fuzzifying said plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and multiplying together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 74
Dependent Claim
82 words
A computer-implemented method for processing a plurality of input signals as a plurality of fuzzy logic rule antecedents, comprising the computer-implemented steps of: receiving a plurality of input data signals; fuzzifying said plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents; and multiplying together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals corresponding to a plurality of fuzzy logic rule consequents.
Claim 75
Dependent Claim
50 words
A method as recited in claim 65, wherein said step of fuzzifying said plurality of input data signals to provide a plurality of fuzzified data signals corresponding to a plurality of fuzzy logic rule antecedents comprises fuzzifying said plurality of input data signals with a first plurality of artificial neurons.
Claim 76
Dependent Claim
48 words
A method as recited in claim 65, wherein said step of multiplying together selected ones of said plurality of fuzzified data signals to provide a plurality of product signals comprises multiplying together selected ones of said plurality of fuzzified data signals with a second plurality of artificial neurons.
Claim 77
Dependent Claim
28 words
A method as recited in claim 65, further comprising the step of receiving and weighting said plurality of product signals to provide a plurality of weighted product signals.
Claim 78
Dependent Claim
42 words
A method as recited in claim 68, wherein said step of receiving and weighting said plurality of product signals to provide a plurality of weighted product signals comprises receiving and weighting said plurality of product signals with a plurality of interneuron weights.
Claim 79
Dependent Claim
21 words
A method as recited in claim 69, wherein said plurality of interneuron weights corresponds to a plurality of fuzzy logic rules.
Claim 80
Dependent Claim
19 words
A method as recited in claim 68, further comprising the step of summing said plurality of weighted product signals.
Claim 81
Dependent Claim
30 words
A method as recited in claim 71, wherein said step of summing said plurality of weighted product signals comprises summing said plurality of weighted product signals with an artificial neuron.
Claim 82
Dependent Claim
88 words
An artificial neural network for processing a plurality of input data in accordance with fuzzy logic, comprising: input neural means for receiving and fuzzifying a plurality of input signals representing numerical data to provide a plurality of fuzzy signals representing fuzzified data; and output neural means coupled to said input neural means for receiving and processing said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 83
Dependent Claim
88 words
An artificial neural network for processing a plurality of input data in accordance with fuzzy logic, comprising: input neural means for receiving and fuzzifying a plurality of input signals representing numerical data to provide a plurality of fuzzy signals representing fuzzified data; and output neural means coupled to said input neural means for receiving and processing said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 84
Dependent Claim
21 words
An artificial neural network as recited in claim 73, wherein said input neural means comprises a first plurality of artificial neurons.
Claim 85
Dependent Claim
21 words
An artificial neural network as recited in claim 73, wherein said output neural means comprises a second plurality of artificial neurons.
Claim 86
Dependent Claim
32 words
An artificial neural network as recited in claim 75, wherein said plurality of numerical fuzzy logic rule consequents comprise a plurality of neural weights associated with said second plurality of artificial neurons.
Claim 87
Dependent Claim
29 words
An artificial neural network as recited in claim 73, further comprising neural summing means coupled to said output neural means for receiving and summing said plurality of numerical signals.
Claim 88
Dependent Claim
18 words
An artificial neural network as recited in claim 77, wherein said neural summing means comprises an artificial neuron.
Claim 89
Dependent Claim
96 words
An artificial neural network for processing a plurality of input data in accordance with fuzzy logic, comprising: a first plurality of artificial neurons which receive and fuzzify a plurality of input signals representing numerical data to provide a plurality of fuzzy signals representing fuzzified data; and a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive and process said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 90
Dependent Claim
96 words
An artificial neural network for processing a plurality of input data in accordance with fuzzy logic, comprising: a first plurality of artificial neurons which receive and fuzzify a plurality of input signals representing numerical data to provide a plurality of fuzzy signals representing fuzzified data; and a second plurality of artificial neurons, coupled to said first plurality of artificial neurons, which receive and process said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 91
Dependent Claim
32 words
An artificial neural network as recited in claim 79, wherein said plurality of numerical fuzzy logic rule consequents comprise a plurality of neural weights associated with said second plurality of artificial neurons.
Claim 92
Dependent Claim
31 words
An artificial neural network as recited in claim 79, further comprising an artificial neuron, coupled to said second plurality of artificial neurons, which receives and sums said plurality of numerical signals.
Claim 93
Dependent Claim
79 words
A computer-implemented method for processing a plurality of input data in accordance with fuzzy logic, comprising the computer-implemented steps of: receiving a plurality of input signals representing numerical data; fuzzifying said plurality of input signals to provide a plurality of fuzzy signals representing fuzzified data; and processing said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 94
Dependent Claim
79 words
A computer-implemented method for processing a plurality of input data in accordance with fuzzy logic, comprising the computer-implemented steps of: receiving a plurality of input signals representing numerical data; fuzzifying said plurality of input signals to provide a plurality of fuzzy signals representing fuzzified data; and processing said plurality of fuzzy signals in accordance with a plurality of numerical fuzzy logic rule consequents in the form of singletons corresponding to unit-dimensional data to provide a plurality of numerical signals.
Claim 95
Dependent Claim
41 words
A method as recited in claim 82, wherein said step of fuzzifying said plurality of input signals to provide a plurality of fuzzy signals representing fuzzified data comprises fuzzifying said plurality of input signals with a first plurality of artificial neurons.
Claim 96
Dependent Claim
52 words
A method as recited in claim 82, wherein said step of processing said plurality of fuzzy signals in accordance with a plurality of fuzzy logic rule consequents which are numerical to provide a plurality of numerical signals comprises processing said plurality of fuzzified data signals with a second plurality of artificial neurons.
Claim 97
Dependent Claim
60 words
A method as recited in claim 82, wherein said step of processing said plurality of fuzzy signals in accordance with a plurality of fuzzy logic rule consequents which are numerical to provide a plurality of numerical signals comprises processing said plurality of fuzzified data signals with a second plurality of artificial neurons and a plurality of neural weights associated therewith.
Claim 98
Dependent Claim
18 words
A method as recited in claim 82, further comprising the step of summing said plurality of numerical signals.
Claim 99
Dependent Claim
28 words
A method as recited in claim 86, wherein said step of summing said plurality of numerical signals comprises summing said plurality of numerical signals with an artificial neuron.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 1999-11-26 | 2000-05-27 | 2000-11-26 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-11-26 | 2004-05-27 | 2004-11-26 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-11-26 | 2008-05-27 | 2008-11-26 | $7700.00 | Window Closed / Lapsed |