title: Robustness evaluation of a minimal RBF neural network for nonlinear-data-storage-channel equalisation
abstract: The authors present a performance-robustness evaluation of the recently developed minimal resource allocation network (MRAN) for equalisation in highly nonlinear magnetic recording channels in disc storage systems. Unlike communication systems, equalisation of signals in these channels is a difficult problem, as they are corrupted by data-dependent noise and highly nonlinear distortions. Nair and Moon (1997) have proposed a maximum signal to distortion ratio (MSDR) equaliser for data storage channels, which uses a specially designed neural network, where all the parameters of the neural network are determined theoretically, based on the exact knowledge of the channel model parameters. In the present paper, the performance of the MSDR equaliser is compared with that of the MRAN equaliser using a magnetic recording channel model, under Conditions that include variations in partial erasure, jitter, width and noise power, as well as model mismatch. Results from the study indicate that the less complex MRAN equaliser gives consistently better performance robustness than the MSDR equaliser in terms of signal to distortion ratios (SDRs)
keyphrases: robustness evaluation RBF neural network nonlinear-data-storage-channel equalisation minimal resource allocation network highly nonlinear magnetic recording channels disc storage systems data-dependent noise highly nonlinear distortions MSDR equaliser MRAN equaliser maximum signal to distortion ratio equaliser digital magnetic recording jitter noise