Design of adaptive Takagi-Sugeno-Kang fuzzy models

Abstract

The paper describes a method of fuzzy model generation using numerical data as a starting point. The algorithm generates a Takagi-Sugeno-Kang fuzzy model, characterised with transparency, high accuracy and small number of rules. The training algorithm consists of three steps: partitioning of the input-output space using a fuzzy clustering method; determination of parameters of the consequent part of a rule from over-determined batch least-squares (LS) formulation of the problem, using singular value decomposition algorithm; and adaptation of these parameters using recursive least-squares method. Three illustrative well-known benchmark modelling problems serve the purpose of demonstrating the performance of the generated models. The achievable performance is compared with similar existing models, available in literature. © 2002 Elsevier Science B.V. All rights reserved.