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The Identification of Dynamic System Based on Memory RBF Neural Network

机译:基于记忆RBF神经网络的动态系统辨识

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The memory RBF (MRBF) neural network (NN) is obtained by introducing memory neuron into the RBF neural network, and it functions a recurrent NN. The MRBF NN can identify dynamic system without feedbacking the past input and output, because the present output is related with the past input. Thus, the NN can identify the system of which the order or delay is unknown. In this paper, the learning algorithm is given and the correlative theory is proved. The simulation of dynamic system identification shows that method is valid, and can provide great potential for self-adaptive control.
机译:记忆RBF(MRBF)神经网络(NN)是通过将记忆神经元引入RBF神经网络而获得的,它具有递归NN的功能。 MRBF NN可以识别动态系统而无需反馈过去的输入和输出,因为当前输出与过去的输入有关。因此,NN可以识别其阶数或延迟未知的系统。给出了学习算法并证明了相关理论。动态系统辨识的仿真表明该方法是有效的,可以为自适应控制提供很大的潜力。

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