On Learning Algorithm Research Of Neural Networks Based On System Identification

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.