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

Abstract

This letter presents the application of the recently developed minimal radial 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 (1997), referred to as maximum signal-to-distortion ratio (MSDR) equalizer. MSDR equalizer uses a specially designed neural architecture where all the parameters are determined theoretically. Simulation results indicate that MRAN equalizer has better performance than that of MSDR equalizer in terms of higher signal-to-distortion ratios.