Self-constructing Fuzzy Neural Networks with Extended Kalman Filter
In this paper, a self-constructing fuzzy neural network employing extended Kalman filter (SFNNEKF) is designed and developed. The learning algorithm based on EKF is simple and effective and is able to generate a fuzzy neural network with a high accuracy and compact structure. The proposed algorithm comprises of three parts: (1) Criteria of rule generation; (2) Pruning technology and (3) Adjustment of free parameters. The EKF algorithm is used to adjust the free parameters of the SFNNEKF. The performance of the SFNNEKF is compared with other learning algorithms in function approximation, nonlinear system identification and time-series prediction. Simulation studies and comparisons with other algorithms demonstrate that a more compact structure with high performance can be achieved by the proposed algorithm.
fuzzy neural networks extended kalman filter function approximation dynamic system identification mackey-glass time-series prediction