document: Robustness evaluation of a minimal RBF neural network for nonlinear-data-storage-channel equalisation The authors present a performance-robustness evaluation of the recently developed minimal resource allocation network -LRB- MRAN -RRB- 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 -LRB- 1997 -RRB- have proposed a maximum signal to distortion ratio -LRB- MSDR -RRB- 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 -LRB- SDRs -RRB-
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