Second Order Diagonal Recurrent Neural Network Algorithm Research And Application

The learning algorithm of recurrent neural network has always been the hot spot of the neural network research, and its application also arouse entensive attention from research enthusiasts. Because dynamic recurrent neural network has the internal self-feedback, the result shows the highly dynamic capability. At present, second-order diagonal recurrent neural network is almost trained by using DBP algorithm, in this paper we will make the further study and discussion about the issues of identification accuracy and convergence speed in gradient descent algorithm.First, the paper introduces the structure of recurrent neural network in detail; and the basic principle of the neural network system identification and network identification model is given.Secondly, as the issue for the gradient search algorithm in second-order diagonal recurrent neural network, an improved gradient descent learning algorithm is proposed, and gives the convergence of improved algorithm.Thirdly, the realization process of DBP algorithm, improved DBP algorithm and RPROP algorithm are given. when identifying nonlinear systems based on second order diagonal recurrent neural network, The simulation results show the identification accuracy and convergence speed based on the DBP algorithm is bigger and slow, in view of the faults of algorithm, improved DBP algorithm is adopted, identification effect is better than the DBP algorithm. But both DBP algorithm and improved DBP algorithm are largely influenced by the gradient.Finally, primarily, RPROP algorithm is applied to train the SDRNN, the simulation results show that compared with the DBP algorithm and improved DBP algorithm, RPROP algorithm for nonlinear system in identification accuracy and convergence speed are superior to the DBP algorithm and improved DBP algorithm.