关键词: 计算机系统结构, 控制理论, 计算智能, 动态递归神经网络, 非线性系统辨识

Abstract: Using an improved adaptive genetic algorithm, an evolutionary Elman neural network model with dynamic regression was built. A learning algorithm which can simultaneously evolve the network structure, the weights, the initial inputs of structural unit, and the selffeedback gain coefficient was proposed. Ultrasonic motor speed identification was performed by the dynamic recurrent network algorithm which was formed by the integration between initial state optimized Elman network with feedback learning algorithm and Elman network online training algorithm. Simulated results show that the proposed algorithms not only realize fully automatic optimization design for the dynamic recurrent neural network, but also improve convergent accuracy for the model identification. And this method can provide a new approach to nonlinear dynamic system identification.

Key words: computer system structure, control theory, computing intelligence, dynamic recurrent neural network, nonlinear system identification