Self-adaptive neuro-fuzzy systems: Structure and learning

This paper presents a systematic and fast learning algorithm for developing a parsimonious internal structure for self-adaptive neuro-fuzzy inference system (SANFIS). The rule extraction problem is cast as a clustering problem so that the number of rules and the number of term sets for input and output variables can be determined in an efficient and systematic way. The consequents of SANFIS could be fuzzy term sets, fuzzy singleton values, or functions of linear combination of input variables. Without a prior knowledge of the distribution of the training data set, the proposed mapping-constrained agglomerative clustering algorithm is able to reveal the true number of clusters and simultaneously estimate the centers and variances of the clusters for constructing an initial SANFIS structure in a single pass. Next, a fast linear/nonlinear parameter optimization algorithm is performed to further accelerate the learning convergence and improve the system performance. Computer simulations show that SANFIS achieves significant improvements in terms of learning convergence, higher accuracy in recognition, and parsimonious system structure.