Dynamic Fuzzy Neural Network (D-FNN)

which basic idea is to construct a RBF neural network based on extension

could be seen as a TSK fuzzy system

as well as a Gaussian RBF neural network based on normalized. Within D-FNN algorithms

not only parameters could be adjusted in the learning process

but also the structure of fuzzy neural network could be automatically determined. Nonlinear parameters are directly decided by the training samples and Gaussian width

which only need one step training to achieve this goal. Due to the application of pruning strategies

network structure would not continue to grow

thus ensuring the generalization capability of the system. Simulations are performed on nonlinear dynamic system identification by using D-FNN

and the effectiveness and efficiency of D-FNN algorithm are proved by comparison with related algorithms.