Referential modes of reasoning

We will propose and study models of referential structures and referential modes of reasoning for fuzzy data. The style of information processing considered there is aimed at reasoning about some global properties (such as, for instance similarity, dominance, inclusion, etc.) of the spaces in which the fuzzy data are situated. The distributed models designed in terms of logic-based neural networks realize the mapping of these properties between the spaces. The scheme of this type embraces reasoning about similarity, difference, dominance, and inclusion of the conclusions that is based on the corresponding relationships between the universes and their strength discerned for the antecedents. For instance, the conclusions issued within the scheme are of the form: b and b′ are similar, b and b′ are different, etc. It will be also clarified how, considering the available degrees of satisfaction of these properties, the corresponding fuzzy sets of conclusion can be determined.