  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.
