However, the scope of this article does not allow us to present Fuzzy Logic formally, nor in its full richness. The terminology embedded in the preceding two lists will become familiar as we work through the Avalanche Predictor example. The model is to be applied when there has been snowfall during the last 24-hour period. Wild slopes do not, of course, have constant pitch and even a measurement of average pitch is approximate. Nor is it clear that the distinction between a number like 15.2 degrees and 17.3 degrees is all that useful.
Fuzzy sets provide a way to incorporate that inherent fuzziness into a model.
These categories are Novice, Intermediate, Advanced and Expert. There is no widely accepted ski industry standard for these terms, but there is an approximate agreement on what they imply. For example, most skiers would consider the pitch range of 0 to 10 degrees as Novice, but there would be less agreement on the angle at which the slope would be considered no longer Novice, but Intermediate. In Figure 2, the fuzzy sets for Intermediate, Advanced and Expert are incorporated as well. Ascertaining the doms of the various input values is called the fuzzification process.