  The advent of multiple readers in magnetic recording opens the possibility of
replacing the current industry's single-track detection with the more promising
multitrack detection architectures. We have proposed a first solution, a
generalized partial-response maximum-likelihood (GPRML) architecture, that
extends the conventional PRML paradigm to jointly detect multiple asynchronous
tracks. In this paper, we propose to replace the conventional
communication-theoretic multiple-input multiple-output equalizer in the GPRML
architecture with a neural network equalizer for better adaption to the
nonlinearity of the underlying channel. We evaluate the proposed equalization
strategy on a realistic two-dimensional magnetic-recording channel, and find
that the proposed equalizer outperforms the conventional linear equalizer, by a
35% reduction in the bit-error rate.
