Classification of antituberculosis herbs for remedial purposes by using fuzzy sets.

Using fuzzy set theory, we created a system, that assesses a herb's usefulness for the treatment of tuberculosis, based on ethnobotanical data. We analysed two systems which contain different amount of inputs. The first system contains four inputs, the second one contains six inputs. We used the Takagi-Sugeno-Kanga model. Mamdani model is poor at representation as it needs more fuzzy rules than that of TSK to model a real world system where accuracy is demanded. It has been employed a fuzzy controller, and a fuzzy model, in successfully solving difficult control and modelling problems in practice. It is implemented in the Fuzzy Logic Toolbox in Matlab. The data for inputs are gathered in the database named SOPAT (selection of plants against tuberculosis), which is part of a project coordinated by the Oxford International Biomedical Centre. In this database there could be up to one million plant species. It would be cumbersome to select a remedy from one (or some) of these species looking at the data base one-by-one. By means of the fuzzy set theory this remedy can be chosen very quickly.