Implementation of recurrent multimodels for system identification

Abstract: Multi-modeling is a recent tool proposed for modeling complex nonlinear systems by the use of a combination of relatively simple set of local models. Due to their simplicity, linear local models are mainly used in such structures. In this work, multi-models having polynomial local models are described and applied in system identification. Estimation of model’s parameters is carried out using least squares algorithms which reduce considerably computation time as compared to iterative algorithms. The proposed methodology is applied to recurrent models implementation. NARMAX and NOE multi-models are implemented and compared to their corresponding neural network implementations. Obtained results show that the proposed recurrent multi-model architectures have many advantages over neural network models.