Patent US5113371A successfully refreshed from Google Patents.
US5249257A
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Fuzzy regression data processing device
Assignee: Panasonic Holdings Corp
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Filing: 1990-08-28
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Granted: 1993-09-28
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Expires: 2010-08-28
Abstract
Abstract A fuzzy data processing device includes a data input portion for inputting data and first and second fuzzy regression model memories for storing first and second fuzzy regression models, respectively. In each fuzzy regression model, all coefficients are denoted as fuzzy values. A fitting degree calculator is provided to calculate a degree of fitting of the first fuzzy regression model to the second fuzzy regression model. A maximum detector detects the maximum of the calculated fitting degree. Thus, the estimated value (y*) is obtained at which the maximum degree of the fitting of fuzzy values Yx and Yz, i.e., the grade peak point of the overlapping portion, is observed.
Inventors
- 👤 Hiroshi Akahori
- 👤 Yo Egusa
- 👤 Isao Hayashi
- 👤 Noboru Wakami
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-30 14:37
CPC Classifications
G; G06; G06N; G06N7/00; G06N7/02; G06N7/023; Y; Y10; Y10S; Y10S706/00; Y10S706/90
Claims Analysis
Total Claims: 6
Claim 1
Independent Claim
81 words
A fuzzy identification system comprising: a data input port; and, a processor coupled to said input port; said processor including: (a) a regression means for identifying a system by regressing an input/output data received on said data input port using a regression formula based on the method of least squares; and (b) a fuzzy regression means for rendering coefficients of said regression formula as fuzzy values in such a manner as to include all the input/output data around said regression formula.
Claim 2
Dependent Claim
81 words
A fuzzy identification system comprising: a data input port; and, a processor coupled to said input port; said processor including: (a) a regression means for identifying a system by regressing an input/output data received on said data input port using a regression formula based on the method of least squares; and (b) a fuzzy regression means for rendering coefficients of said regression formula as fuzzy values in such a manner as to include all the input/output data around said regression formula.
Claim 3
Dependent Claim
177 words
A fuzzy data processing device comprising: a data input port; a first memory coupled to said data input port, said first memory having means for storing therein a first fuzzy regression model in which all coefficients of the fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a first condition being included in said first fuzzy regression model; a second memory coupled to said data input port, said second memory having means for storing therein a second fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a second condition being included in said second fuzzy regression model; and, a processor coupled to said first and second memories; said processor comprising: (a) a fitting degree calculating means for calculating a degree of fitting of said first fuzzy regression model to said second fuzzy regression model; and (b) a maximum detector means for detecting the maximum of the calculated fitting degree, whereby an estimated value (Y*) is obtained.
Claim 4
Dependent Claim
177 words
A fuzzy data processing device comprising: a data input port; a first memory coupled to said data input port, said first memory having means for storing therein a first fuzzy regression model in which all coefficients of the fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a first condition being included in said first fuzzy regression model; a second memory coupled to said data input port, said second memory having means for storing therein a second fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a second condition being included in said second fuzzy regression model; and, a processor coupled to said first and second memories; said processor comprising: (a) a fitting degree calculating means for calculating a degree of fitting of said first fuzzy regression model to said second fuzzy regression model; and (b) a maximum detector means for detecting the maximum of the calculated fitting degree, whereby an estimated value (Y*) is obtained.
Claim 5
Dependent Claim
190 words
An abnormal fuzzy data detecting device comprising: a data input port; a first memory coupled to said data input port, said first memory having means for storing therein a first fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a first condition being included in said first fuzzy regression model; a second memory coupled to said data input port, said second memory having means for storing therein a second fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a second condition being included in said said second fuzzy regression model; and, a processor coupled to said first and second memories; said processor comprising: (a) a fitting degree calculating means for calculating a degree of fitting of said first fuzzy regression model to said second fuzzy regression model; and (b) a fitting degree comparator means for comparing the calculated fitting degree with a predetermined degree to detect an abnormal condition when said calculated fitting degree is smaller than said predetermined degree.
Claim 6
Dependent Claim
190 words
An abnormal fuzzy data detecting device comprising: a data input port; a first memory coupled to said data input port, said first memory having means for storing therein a first fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a first condition being included in said first fuzzy regression model; a second memory coupled to said data input port, said second memory having means for storing therein a second fuzzy regression model in which all coefficients of a fuzzy regression model are denoted as fuzzy values, and with all the data obtained under a second condition being included in said said second fuzzy regression model; and, a processor coupled to said first and second memories; said processor comprising: (a) a fitting degree calculating means for calculating a degree of fitting of said first fuzzy regression model to said second fuzzy regression model; and (b) a fitting degree comparator means for comparing the calculated fitting degree with a predetermined degree to detect an abnormal condition when said calculated fitting degree is smaller than said predetermined degree.
References Cited (Backward Citations)
No citation records indexed.
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1996-09-27 | 1997-03-29 | 1997-09-28 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2000-09-27 | 2001-03-29 | 2001-09-28 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2004-09-27 | 2005-03-29 | 2005-09-28 | $7700.00 | Window Closed / Lapsed |