An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data

Designing a fuzzy inference system (FIS) from data can be divided into two main phases: structure identification and parameter optimization. First, starting from a simple initial topology, the membership functions and system rules are defined as specific structures. Second, to speed up the convergence of the learning algorithm and lighten the oscillation, an improved descent method for FIS generation is developed. Furthermore, the convergence and the oscillation of the algorithm are systematically analyzed. Third, using the information obtained from the previous phase, it can be decided in which region of the input space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased. Consequently, this produces a new and more appropriate structure. Finally, the proposed method is applied to the problem of nonlinear function approximation.