A Generalized Online Self-constructing Fuzzy Neural Network

We propose a Generalized Online Self-constructing Fuzzy Neural Network (GOSFNN) 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 incorporated into structure identification which implements an optimal and compact network structure. The GOSFNN 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 Extended Kalman Filter (EKF) method. The performance of the GOSFNN paradigm is compared with other well-known algorithms like ANFIS, OLS, GDFNN, SOFNN and FAOS-PFNN, etc., on a benchmark problem of multi-dimensional function approximation. Simulation results demonstrate that the proposed GOSFNN approach can facilitate a more powerful and parsimonious FNN with better performance of approximation and generalization.