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.
葛洪伟，王士同. 可能性线性模型中的参数选优与输入噪声间关系的研究[J]. 模式识别与人工智能, 2007, 20(1): 42-47.
GE HongWei, WANG ShiTong. Research on the Dependency between Optimal Parameter and the Input Noise in Possibilistic Linear Model. , 2007, 20(1): 42-47.
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