A Generalized Online Self-organizing Fuzzy Neural Network for Nonlinear Dynamic System Identification

In this paper, we propose a Generalized Online Self-organizing Fuzzy Neural Network (GOSFNN) for nonlinear dynamic system identification. The GOSFNN extends the ellipsoidal basis function (EBF)-based fuzzy neural networks (FNNs) by permitting input variables to be modeled by dissymmetrical Gaussian functions (DGFs). Due to the flexibility and dissymmetry of left and right widths of the DGF, the partitioning made by DGFs in the input space is more flexible and more economical, and therefore results in a parsimonious FNN with high performance under the online learning algorithm. The geometric growing criteria and the error reduction ratio (ERR) method are used as rule growing strategies to realize the structure learning algorithm which implements an optimal and compact network structure. The proposed GOSFNN starts with no hidden neurons and does not need to partition the input space a priori. In addition, all the free parameters in premises and consequents are online adjusted by using the Extended Kalman Filter (EKF) approach. The performance of the proposed GOSFNN paradigm is compared with other well-known algorithms like OLS, RBF-AFS, DFNN, GDFNN and FAOS-PFNN, etc., on a benchmark problem in the field of nonlinear dynamic system identification. Simulation results demonstrate that the proposed GOSFNN approach would be able to facilitate a more powerful and more economical fuzzy neural network with better identification performance.