FLAS: a fuzzy linear adaptive system for identification of non-linear noisy functions

FLAS (fuzzy linear adaptive system) is a self-organizing fuzzy system for non-linear function identification, that uses a learning method based on clustering to generate fuzzy rules and tune their parameters. This method reduces the influence of pattern presentation order, permits building prototypes with physical meaning, allows measuring the importance of each variable, and therefore reduces the influence of noise. FLAS fuzzy membership functions are defined as barycentric coordinates in a simplex, yielding equivalence between Mandami and Takagi-Sugeno defuzzification methods. This allows FLAS to make piecewise linear interpolation and thus facilitates a rule fusion procedure. In simulations done for noisy non-linear function identification tasks, FLAS showed better results than other comparative systems yielding smaller identification error and number of rules. In the difficult task of bioprocesses variable identification FLAS also outperforms other systems. FLAS theoretical features and good identification performance provide good expectations for its implementation within different model based controllers