A Singular-Value-QR Decomposition Based Method for Training Fuzzy Logic Systems in Uncertain Environments

Issue title: Fuzzy Signal and Image Processing

Article type: Research Article

Authors: Mouzouris, George C. | Mendel, Jerry M.

Affiliations: Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089-2564, USA

Abstract: We extend the theory of nonsingleton fuzzy logic systems (NSFLSs) by presenting an algorithm to design and train such systems. Since NSFLSs are a generalization of singleton fuzzy logic systems, the algorithm is equally applicable to both types of systems. The proposed SVD-QR method selects subsets of independent basis functions which are sufficient to represent a given system, through operations on a nonsingleton fuzzy basis function matrix. In addition, it provides an estimate of the number of necessary basis functions. We present several examples to illustrate the ability of the SVD-QR method to operate in uncertain environments.

DOI: 10.3233/IFS-1997-5408

Journal: Journal of Intelligent and Fuzzy Systems, vol. 5, no. 4, pp. 367-374, 1997