Use of multilayer recursive model for non-linear dynamic system identification

Abstract

Abstract In practice, the dynamics of the system are uncertain due to nonlinear and dynamic characteristics. It is difficult to establish accurate identification and prediction of the nonlinear plants that require dynamic modelling of the system. Extreme learning machine (ELM) as the recursive model due to its fast training and convergence speed is utilized in this work. However, its limitation is that it has only 1 hidden neuron which tends to make evolution speed low. Further, Multi-layer ELM (ML-ELM) model is applied on a nonlinear Auto-regressive complex benchmark system. The performance of ML-ELM is compared with dynamic recurrent functional link neural network (DRFLNN), functional link neural network (FLNN), nonlinear auto-regressive moving average (NARAX), multi-layer perception (MLP), radial basis function network (RBFN), Elman recurrent neural network (ERNN), and basic ELM models. From the comparison table, it can be seen that ML-ELM has better performance as compared with other models.