An online approach towards self-generating fuzzy neural networks with applications

In this paper, a novel approach towards self-generating fuzzy neural network (SGFNN) is proposed. The proposed approach is simple and effective and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is applied for function approximation, nonlinear system identification and time-series prediction problems. Simulation results and comparative studies with other algorithms demonstrate that a more compact architecture with high performance can be obtained by the proposed approach.