A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

In this paper, a fast approach for automatically generating fuzzy rules fro m sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis function and functionally is equivalent to a Takagi-Sugeno-Kang fuzzy system. The salien t characteristics of the GD-FNN are: 1) structure identification and parame ters estimation are performed automatically and simultaneously without part itioning input space and selecting initial parameters a priori; 2) fuzzy ru les can be recruited or deleted dynamically; 3) fuzzy rules can be generate d quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. The GD-FNN is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time-varying drug delivery system a nd multilink robot control. Simulation results demonstrate that a compact a nd high-performance fuzzy rule-base can be constructed. Comprehensive compa risons with other latest approaches show that the proposed approach is supe rior in terms of learning efficiency and performance.