Nonlinear System Identification Based on an Online SCFNN With Applications in IoTs

Abstract

In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.