Nonlinear magnetic storage channel equalization using minimal resource allocation network (MRAN)

This Letter presents the application of the recently developed minimal radi al basis function neural network called minimal resource allocation network (MRAN) for equalization in highly nonlinear magnetic data storage channels . Using a realistic magnetic channel model, MRAN equalizer's performance is compared with the nonlinear neural equalizer of Nair and Moon, referred to as maximum signal to distortion ratio (MSDR) equalizer. MSDR equalizer use s a specially designed neural architecture where all the parameters are det ermined theoretically. Simulation results indicate that MRAN equalizer has better performance than that of MSDR equalizer in terms of higher signal to distortion ratios (SDRs).