A novel DFNN(dynamic fuzzy neural networks )based on TSK(Takagi-Sugeno-Kang) fuzzy model was presented to the nonlinear dynamic system. The DFNN was constitutive of static networks and dynamic networks. The static networks realized premise and defuzification part. The recurrent dynamic networks realized by FIR filter was used for realizing consequence part. Beside this a new algorithm, constrained optimization method-FUNCOM(fuzzy neural constrained optimization method)was suggested for reducing the convergence time of networks parameter. The network training task was formulated as a constrained optimization problem. The proposed dynamic model equipped with the learning algorithm was applied in a nonlinear dynamic system. Comparisons with other FNN(fuzzy neural network) and DFNN (dynamic fuzzy neural network)were given and discussed, indicating the effectiveness of the DRFNN(dynamic recurrent fuzzy neural network) and the algorithm.