Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems

This study considers a functional link neural network (FLNN) structure for identifying nonlinear dynamic systems. We tackle the problem of system identification in noisy environments by introducing an adaptive tuning structure based on individual particle optimization (IPO) for the nonlinear systems identification via functional link neural network. The IPO algorithm is applied in order to train the FLNN and achieve the optimum weights of the network for efficiently identifying the nonlinear systems. The proposed optimized FLNN is tested through several experiments, including real-time identification of some nonlinear dynamic systems. Finally, we develop a comparison between the results with the previous counterpart optimized FLNN based LMS, BP, and some evolutionary (GA, PSO, CLPSO) training algorithms. Simulation results verify that the proposed optimization technique, IPO, outperforms these algorithms in the sense of speedup and performance. The remarkable issue addressed here is introducing the IPO algorithm as a real-time optimal tuning technique, which is applicable in other real-time adaptive structures.