Robust Adaptive Filtering Based on Exponential Functional Link Network
This work addresses the problem of robust nonlinear filtering for systems operating under impulsive interference, which is important for engineers working with real-world noisy data.
This paper proposes an adaptive Exponential Functional Link Network (EFLN) filtering algorithm, EFLN-ISR, which utilizes a novel inverse square root (ISR) cost function. The algorithm demonstrates robust learning capability even in the presence of impulsive interference, and its performance is validated through simulations and experimental results in hysteretic nonlinear system identification.
The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification.