【作者】 王立红；

【导师】 胡耀华；

【作者基本信息】 大连海事大学 ， 通信与信息系统， 2002， 硕士

【摘要】 本文对神经网络用于系统辨识的参数学习算法进行了系统的研究，简要介绍了神经网络系统辨识的基本原理，分析了线性神经网络在系统辨识中的应用，并将其用于直流调速系统建模。 BP网络是应用最广的一种网络，本文对BP算法进行了详细的推导，分析了其存在的缺陷；改进的BP算法虽然能在一定程度上提高学习速度，但对改善收敛效果，提高收敛精度，甚至摆脱局部极小，没有太大的积极作用。因此，作者将非线性规划的平行切线算法用于神经网络的权值学习，提出了一种具有快速学习算法的神经网络。 本文分析了一种动态补偿神经网络模型，模型的训练利用反向传播原理实现。采用该模型对非线性动态系统进行建模时，能显著提高建模精度，特别是在网络模型工作时，对新出现的输出误差具有动态补偿能力。 分析了动态递归神经网络系统辨识的参数学习算法。 直流调速系统应用范围广泛，传统的建模方法采用近似的线性化方法，忽略了各环节的非线性因素，这将使模型存在一定的误差，导致其所应用领域的系统性能降低。为了改变这一状况，有必要研究调速系统的非线性特性。本文采用基于RPE算法的神经网络对速度环进行动态辨识，仿真结果表明，神经网络能满意地建立直流调速系统的动态模型。更多还原

【Abstract】 This paper makes a systematic research on parameter learning algorithm, which applies the neural network to system identification and modeling of D. C. speed-adjustable system. Besides, the basic principle of system identification using neural network and the application of linear neural network in it are briefly mentioned.BP network is one of the most widely used ones. Standard BP algorithm is derived in details here, and its existent defects are also analyzed. Though modified BP algorithm can enhance learning rate to some extent, its active function is far from satisfactory on improving convergence effect and raising convergence accuracy and even geting rid of local extreme minimum value. This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.A neural network model with dynamical compensating capability is analyzed. During the training of this network model, we apply the principle of dynamic error back-propagation. Using this model in nonlinear of dynamic systems modeling, the modeling accuracy can be significantly raised and the dynamic error can be effectively reduced especially during working of this network model.The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed.D. C. speed-adjustable system is widely adopted. The traditional modeling approach adopts approximately linearized method and neglects nonlinear factors of links, which causes the model to produce certain error and leads to the decreasing of system performance in its usage. In this sense, it is advisable to study the nonlinear nature of D.C. speed-adjustable system. In this paper, the speed loop is identified dynamicallyusing the neural network based on RPE algorithm. The simulation results imply that the neural network can develop the dynamic model of D.C. speed-adjustable satisfactorily.更多还原