Fuzzy classes

CNCL provides a set of classes for building fuzzy inference engines. Available are fuzzy sets, fuzzy variables, and a inference engine based on fuzzy rules. The membership values are normalized ( by default ), but can be changed to max and min values in different classes. All fuzzy sets and functions are realized with crisply defined membership values ( type 1 fuzzy sets and functions ).
Currently prod-min inference is used and a center-of-gravity output defuzzification. This will be extended in a future release, providing different operators for aggregation, inference, and accumulation. Up to now two different fuzzy set representations are implemented: LR-representation and arrays.

For more information about fuzzy logic see:
Zimmermann,. H.-J. [1991]. Fuzzy Set Theory And Its Applications. Kluwer Accademic Publishers.
Additionally, the FAQ of the newsgroup "comp.ai.fuzzy" should be recommended in this context.