An SVD-based fuzzy model reduction strategy
J. Yen, Laing Wang · Proceedings of IEEE 5th International Fuzzy Systems · 2002
This paper describes a novel fuzzy model reduction approach for overcoming the curse of dimensionality associated with high-dimensional data modeling problems. A numerically reliable orthogonal transformation technique, known as the singular value decomposition (SVD), is utilized to detect and select the dominant fuzzy rules from a rule base. The effectiveness of the proposed approach is illustrated using a nonlinear limit cycle modeling problem.