The input data for calculation are standard raster data stored in the GRASS database. The system uses one or more maps of any type (i.e integer, float, double precision) and accepts all values appropriate to the given data type. Any cell having a null value is ignored during data processing. The output is one (or optionally more) floating point raster map containing the final result. The analysis involves continuous data processing and does not require intermediate stages. The input information is passed to the system as a text file. The file contains map names used in the analysis (analogue of linguistic variables) and definitions of fuzzy sets (analogue of linguistic values) for every map. The map file also includes one output map definition. To identify the output map in the rule file, it must be named _OUTPUT_, while the output map name is passed as a standard command parameter (see Appendix). The range of all sets in the output map is used to define the universe of the consequent, while the resolution of the universe is also a command parameter.
Membership modelling is very important in fuzzy logic concepts. In the proposed system, modelling of a fuzzy set is limited to a generalised trapezoid but with additional modification possibilities. It is also possible to define one-sided sets with only two points and information as to whether the points define the left or right boundary of the set. The shape of the boundary of the set may be “linear”, “s-, g- or j-shaped”. Additional modifiers are hedge and height. If the hedge parameter is other than from zero, dilatation (negative) or concentration (positive) is applied on the defined fuzzy set as many times as the number in the hedge field. The height parameter defines the maximum membership of the set. Height affects both the maximum membership of the set and its boundaries. This approach allows precise modelling of fuzzy set boundaries without visual feedback. Examples of fuzzy sets based on different definitions are shown in Fig. 1.