Patent
1990-01-17
1992-12-01
Fleming, Michael R.
395 21, 395900, G05B 1300
Patent
active
051685493
DESCRIPTION:
BRIEF SUMMARY
TECHNICAL FIELD
The present invention relates to inference rule determining methods and inference devices in controlling apparatus, in the inference of an expert system, and in pattern recognition based on input data.
BACKGROUND ART
In order to describe a conventional technique, first, the basic fuzzy inference will be outlined by taking as an example fuzzy control used in apparatus control, etc.
In a control system which relates to the evaluation of human beings, the operator can determine a final manipulated control variable using a variable which the operator has determined subjectively and/or sensuously, for example, "large", "middle", "tremendously", or "a little" (which is hereinafter referred to as a fuzzy variable). In this case, the operator determines the manipulated variable from the input variables on the basis of his control experience. An inference device using fuzzy control assigns an input fuzzy number to an IF part of an inference rule in accordance with the inference rule of "IF. . .THEN. . ." type and determines an output fuzzy number of the THEN part from a fitting grade (membership value) indicative of the extent to which the inference rule is satisfied. The actual manipulated variable can be obtained by taking the center of gravity value, etc., of the output fuzzy number.
One of the conventional control methods using fuzzy inference is fuzzy modeling disclosed, for example, in Gean-Taek Kang, Michio Sugano; "fuzzy modeling" SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS PAPERS, Vol. 23, No. 6, pp. 650-652, 1987. In the control rule of the fuzzy modeling, the IF part is constituted by a fuzzy proposition and the THEN part is constituted by a regular linear equation between inputs and outputs. If a timing lag of first order tank model is considered, for example, the control rule among a control error e, its change in error de and a control output (manipulated variable) u is given by
A plurality of such inference rules are prepared, all of which are referred to as control rules. Zero, Positive Medium, etc., are each a label or a fuzzy variable (fuzzy number) used to described the rules. FIG. 1 illustrates one example of a fuzzy variable. In FIG. 1, NB denotes Negative Big; NM, a negative Medium; NS, a Negative Small; ZO, a Zero; PS, Positive Small; PM, a Positive Medium; and PB, Positive Big. A function indicative of a fuzzy number F on X is referred to as a membership function u.sub.f ( ) and the function value of x.sup.0 is referred to as a membership value u.sub.F (X.sup.0). The general form of the control rules is given by ##EQU1## where R.sup.5 indicates a s.sup.th rule; x.sub.j, an input variable; A.sub.j.sup.s, a fuzzy variable; y.sup.s, an output from the s.sup.th rule; and c.sup.s, a THEN part parameter. The result of inference for an input (x.sub.1.sup.0, x.sub.2.sup.0, . . ., x.sub.m.sup.0) is given by ##EQU2## where n is the number of rules, and w.sup.s is a fitting grade at which the input (x.sub.1.sup.0, x.sub.2.sup.0, . . ., x.sub.m.sup.0) is adapted to the IF part of the S.sup.th rule W.sup.s is given by ##EQU3## where the membership value in the x.sup.0 in the fuzzy variable A.sub.j.sup.s is u.sub.Aj.sup.s (x.sub.j). The identification of a fuzzy model includes a two-stage structure, namely, identification of the structure of the IF and THEN parts and identification of the IF and THEN parts. The conventional identifying process includes the steps of (1) changing the fuzzy proposition of the IF part to a proper proposition, (2) changing W.sup.s in a constant manner, (3) searching only the actually required ones of the input variables of the THEN part using a backward elimination method, (4) calculating parameters of the THEN part using the method of least squares, (5) repeating the steps (2)-(4) to determine an optimal parameter, (6) changing the fuzzy proposition of the IF part and (7) returning to the step (2) where an optimal parameter is repeatedly searched under the conditions of a new fuzzy proposition. Namely, this method can be said to be a heuristic method-like
REFERENCES:
patent: 4837725 (1989-06-01), Yamakawa
Hayashi et al., "Neural-Network-Driven Fuzzy Reasoning Model", Proc. 28th SICE Annul. Conf., Jul. 1989, 1343-1346.
Procyk et al., "A Linguistic Self-Organizing Controller", Automatica, vol. 15, pp. 15-30 Jan. 19789.
"Fuzzy Modeling" Society of Instrument and Control Engineers Papers, vol. 23, No. 6, pp. 650-652, 1987; Gean-Taek Kang, Michio Sugeno.
"Revised GMDH Algorithm Estimating Degree of the Complete Polynomial", Society of Instrument and Control Engineers Papers, vol. 22, No. 9, pp. 928-934, 1986.
D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Representations by Back-Propagating Errors", Nature, vol. 323, pp. 523-536, Oct. 9, 1986.
Hayashi Isao
Takagi Hideyuki
Downs Robert W.
Fleming Michael R.
Matsushita Electric - Industrial Co., Ltd.
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