Neural Network-Based Moving Window Iterative Nonlinear System Identification

The finite memory estimation based learning algorithm (FME-LA (Kang et al., 2019)) is a newly developed technique for identifying unknown nonlinear systems that demonstrates remarkable accuracy and robustness against disturbances and error accumulation by utilizing a finite number of measurements on a moving window. However, there are several drawbacks to FME-LA (Kang et al., 2019). First, the weights of higher-order or complex systems cannot be learned because it only learns the output layer's weights. Second, it does not consider noise statistics, resulting in significant performance degradation in complex environments. Finally, it is not suitable for real-time implementation because the computation increases rapidly with the size of the moving window. To address these limitations, we propose the moving window iterative nonlinear system identification (MWI-NSID) scheme, which guarantees robustness and accuracy with a significant reduction in computation. In simulation results on a complex nonlinear system, the proposed MWI-NSID scheme demonstrated robustness, accuracy, and significantly reduced computation.