A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network

In this paper, we propose a Generalized ellipsoidal basis function based online self-constructing fuzzy neural network (GEBF-OSFNN) which 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 interpretable, 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 growing and pruning strategies respectively to realize the structure learning algorithm which implements an optimal and compact network structure. The GEBF-OSFNN starts with no hidden neurons and does not need to partition the input space a priori. In addition, all free parameters in premises and consequents are adjusted online based on the ε-completeness of fuzzy rules and the linear least square (LLS) approach, respectively. The performance of the GEBF-OSFNN paradigm is compared with other well-known algorithms like RAN, RANEKF, MRAN, ANFIS, OLS, RBF-AFS, DFNN, GDFNN GGAP-RBF, OS-ELM, SOFNN and FAOS-PFNN, etc., on various benchmark problems in the areas of function approximation, nonlinear dynamic system identification, chaotic time-series prediction and real-world benchmark problems. Simulation results demonstrate that the proposed GEBF-OSFNN approach can facilitate a more powerful and more parsimonious FNN with better performance of approximation and generalization.