Conventional training methods for diagonal recurrent neural networks identifier are limited to the first and second derivative methods. In this paper, a novel training algorithm based on evolutionary programming (EP) and particle swarm optimization (PSO) for evolutionary diagonal recurrent neural network (EDRNN) is proposed. Meanwhile, a new select mode is given for improving the premature convergence for PSO. Compared with conventional methods, EDRNN has prominent advantage in identifying nonlinear dynamic systems because the structure and weight of EDRNN can be evolved simultaneously. Experimental results of identifying the classical nonlinear dynamic systems confirm that EDRNN-based method is a promising tool for identifier.