Inference rule determining method and inference device

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395 22, 395 21, 395900, G05B 1300

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active

052553442

ABSTRACT:
An inference rule determining process according to the present invention sequentially determines, using a learning function of a neural network model, a membership function representing a degree which the conditions of the IF part of each inference rule is satisfied when input data is received to thereby obtain an optimal inference result without using experience rules. The inventive inference device uses an inference rule of the type "IF . . . THEN . . ." and includes a membership value determiner (1) which includes all of IF part and has a neural network; individual inference quantity determiners (21)-(2r) which correspond to the respective THEN parts of the inference rules and determine the corresponding inference quantities for the inference rules; and a final inference quantity determiner which determines these inference quantities synthetically to obtain the final results of the inference. If the individual inference quantity determiners (2) each has a neural network structure, the non-linearity of the neural network models is used to obtain the result of the inference with high inference accuracy even if an object to be inferred is non-linear.

REFERENCES:
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Lippmann, R. P., "An Intro. to Computing with Neural Nets," IEEE ASSP Mag., Apr. 1987, 4-22.
Takagi et al., "Artificial Neural Network Driven Fuzzy Reasoning", Intl. Workshop on Fuzzy System Applications, Aug. 1988, 217-218.
"Fuzzy Modeling" Society of Instrument and Control Engineers Papers, vol. 23, No. 6, pp. 650-652, 1987; Gean-Taek Kang, Michio Sugeno.
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D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Representations by Back-Propergating Erros", Nature, vol. 323, pp. 523-536, Oct. 9, 1986.
Isao Hayashi, et al, "Formulation of Fuzzy Reasoning by neural Network", 4th Fuzzy Symposium (Tokyo, May 30-31, 1988), pp. 55-60.

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