Constructing Neuro-Fuzzy Systems with TSK Fuzzy Rules and Hybrid SVD-Based Learning

In this paper, an architecture of fuzzy neural networks with Takagi-Sugeno-Kang (TSK) fuzzy rules is proposed. A novel learning algorithm [1]-[2] with self-organizing ability and fast learning rules is also presented. In the structure identification phase of our method, fuzzy IF-THEN rules are extracted with a self-constructing rule generation algorithm. In the parameter identification phase, a hybrid learning algorithm is used, in which the consequent parameters are derived optimally by a recursive SVD-based least squares estimator (RSVD) and the precondition parameters are tuned by the backpropagation algorithm. Simulation results have demonstrated that a more compact structure with a faster convergence rate and smaller mean square errors can be achieved by the proposed approach.