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- W2017694002 abstract "Abstract The paper argues in favour of reasoning methods that process positional information, i.e. information about the position of the input with respect to the class prototype, and thus achieve a better specification of the corresponding output class. It is shown that such an approach leads to reducing or cancelling unwanted nonlinearities (of input-output characteristic) imposed by reasoning methods, nonlinearities that alter the dependence expressed in the knowledge base. Two methods which use positional information are presented: (a) the mapping of o-cut borders (MACB) (Stoica, 1993, Proc. Fifth IFSA World Congress, pp. 1266-1269), inspired from a particular interpretation of the representation theorem and (b) the Left/Right Centre of Gravity (L/R COG) (Ghanayem, 1995, Proc. IEA/AIE, pp. 41 -45). The methods are tested in implementing a power system stabiliser (PSS) Simulation results show better performance of methods that use positional information, as compared with classic Mamdani (Mamdani, 1975, International Journal of Man-Machine Studies, 7, 1-13) reasoning. Reflections on a “best” reasoning method conclude the paper." @default.
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- W2017694002 date "1999-08-01" @default.
- W2017694002 modified "2023-10-02" @default.
- W2017694002 title "FUZZY REASONING USING POSITIONAL INFORMATION" @default.
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- W2017694002 doi "https://doi.org/10.1080/03081079908935234" @default.
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