DRNN Nonlinear System Identification Based On RPROP-SVR Hybrid Algorithm

The identification of nonlinear system is always the difficulty and the focus of control theory study. When identifying the complex nonlinear system model based on the diagonal recurrent neural network, dynamic mapping for nonlinear system is available via adjusting internal neurons weight. The results show the highly dynamic mapping capability as well as the little regulation for network weight. But due to existing learning algorithm of the diagonal recurrent neural network being defects of slow convergence and bad identification inaccuracy, so this paper discussed and researched the topic.As the large identification errors and the slow convergence rate for dynamic back propagation (DBP) algorithm in the diagonal recurrent neural network, Lyapunov function algorithm (LFA) and genetic algorithm (GA) here are developed as an improved algorithm to train the diagonal recurrent neural network. The identification results are fully compared to demonstrate that both the identification errors and the convergence rate for LFA are better than that of DBP algorithm and GA, the identification residual is the smallest, and the convergence rate is the most fast.Secondly, in order to avoid the gradient affection for network weight in Lyapunov function, a local adaptive learning algorithm, namely resilient back-propagation (RPROP) algorithm, is applied to train the diagonal recurrent neural network, which can almost neglected the gradient variation but determine the adjustment direction for the network weight. Therefore, RPROP algorithm has the advanced characteristics as high identification precision, convergence-acceleration capacity and appropriate solution for local minimum problem.According to problem of selecting the number of hidden neural network nodes by experience, a hybrid algorithm named RPROP-SVR is proposed by integrating RPROP algorithm and vector support regression (SVR) algorithm to train the diagonal recurrent neural network. Detailedly, the number of hidden neural network nodes is selected by SVR algorithm, as well as that network weight is trained by RPROP algorithm. Furthermore, this new algorithm shows that expected identification effect has been obtained with its application in a nonlinear identification system.Finally, the identification effects for RPROP algorithm and RPROP-SVR algorithm, respectively, are fully compared basing on the simulation results that the identification errors, the identification precision and the convergence speed are almost the same. Therefore, RPROP-SVR hybrid algorithm is demonstrated to automatically effectively determine the number of hidden neural network nodes. Interestingly, this technique can be an alternative way of artificial test to automatically build the structure of a neural network at a time, which improves the efficiency a lot without artificial test of the number of hidden neural network nodes.