Fuzzy reasoning using positional information

The paper argues in favour of reasoning methods that process positional inf ormation, i.e. information about the position of the input with respect to the class prototype, and thus achieve a better specification of the corresp onding output class. It is shown that such an approach leads to reducing or cancelling unwanted nonlinearities (of input-output characteristic) impose d by reasoning methods, nonlinearities that alter the dependence expressed in the knowledge base. Two methods which use positional information are pre sented: (a) the mapping of oi-cut borders (MACB) (Stoica, 1993, Proc. Fifth IFSA World Congress, pp. 1266-1269), inspired from a particular interpreta tion of the representation theorem and (b) the Left/Right Centre of Gravity (L/R COG) (Ghanayem, 1995, Proc. IEA/AIE, pp. 41-45). The methods are test ed in implementing a power system stabiliser (PSS). Simulation results show better performance of methods that use positional information, as compared with classic Mamdani (Mamdani, 1975, International Journal of Man-Machine Studies, 7, 1-13) reasoning. Reflections on a "best" reasoning method concl ude the paper.