Fuzzy model approximation and its SVD reduction

This paper is concerned with the argument that the identification of Takagi Sugeno (T-S) fuzzy models from training data should involve an important feature between data fitness and model complexity. One hand a (T-S) fuzzy model with a large number of fuzzy rules may encounter the risk of having an approximation capable of fitting training data well. On the other hand it may be difficult to run this fuzzy model structure due to heavy computational cost. In order to facilitate the development of a balance between these requirements, a Higher Order Singular Value Decomposition (HOSVD) based T-S fuzzy model reduction is introduced using the well-known Yam SVD fuzzy rule-based approximation technique.