Summary: This paper presents a novel Adaptive Variable Structure (AVS) method to design a Fuzzy Neural Network (FNN). This AVS-FNN is based on Radial Basis Function (RBF) neurons, which have center and width vectors. The network performs sequential learning through sliding data window reflecting system dynamic changes, and dynamic growing-and-pruning structure of FNN. The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a-priori. The structure-learning approach relies on the contribution of the size of the output. (2) A set of fuzzy rules can be inserted or reduced during the learning process. (3) The connection weighting factors between the deduction layer and output layer generated quickly without resorting to iteration learning are updated by the least-squares algorithm. The proposed method effectively generates a fuzzy neural model with a highly accurate and compact structure. Simulation results demonstrate that the proposed AVS-FNN has a self-organizing ability, which can determine the structure and parameters of the FNN automatically. The application of this new approach has been applied successfully in the 3 DOF helicopter systems, showing the effectiveness and potential of the proposed design techniques.