Abstract
An architecture for the implementation of possibilistic models in an object-oriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilistic processes are specified. Supplementary methods—including the fast Möbius transform, the maximum entropy and Bayesian approximations of random sets, distribution operators, compatibility measures, consonant approximations, frequency conversions, and possibilistic normalization and measurement methods—are also introduced. Empirical results to be investigated are also described.
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Joslyn, C. (1996). An object-oriented architecture for possibilistic models. In: Klir, G.J., Ören, T.I. (eds) Computer Aided Systems Theory — CAST '94. Lecture Notes in Computer Science, vol 1105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61478-8_69
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DOI: https://doi.org/10.1007/3-540-61478-8_69
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