A Singular-Value-QR Decomposition Based Method for Training Fuzzy Logic Systems in Uncertain Environments
George C. Mouzouris, Jerry M. Mendel · Journal of Intelligent & Fuzzy Systems · 1997
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.