A comparative study of different methods for realizing DFNN algorithm

Presents a comparative study of different methods for realizing the basic learning algorithm of dynamic fuzzy neural networks (DFNNs). Performances of the least squared estimation, Kalman filter and extended Kalman filter methods used for weight adjustment in the basic learning algorithm of DFNNs in terms of learning speed, neuron requirement, approximation accuracy and noise immunity are evaluated and compared.