A fuzzy neural network approximator with fast terminal sliding mode and its applications
Shuanghe Yu, Xinghuo Yu, Zhihong Man · 2004
This paper presents a novel training method for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions. The training algorithm uses the principle of the fast terminal sliding mode (TSM) into the conventional gradient descent (GD) learning algorithm. It guarantees that the approximation is stable and converges to the optimal approximation function with improved speed. The proposed FNN approximator is then applied in the control of an unstable nonlinear system and the Duffing system. The simulation results demonstrate the effectiveness of the proposed method.