Abstract Possibilistic linear model (PLM) based on possibility theory plays a pivotal role in fuzzy modeling. In order to enhance the generalization capability of the linear model, the regularized version is firstly extended, i.e. the regularized possibilistic linear model (RPLM). Then the RPLM is transformed into the corresponding equivalent MAP problem. Accordingly, with a series of mathematical derivation, the inversely proportional dependency between the parameter and the standard deviation of Gaussian noisy input is revealed. In the meanwhile, the simulation result has proved this conclusion. Obviously, the conclusion is helpful for the practical applications of both PLM and RPLM.
GE HongWei,WANG ShiTong. Research on the Dependency between Optimal Parameter and the Input Noise in Possibilistic Linear Model[J]. , 2007, 20(1): 42-47.
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