  Based on our recent research on neural heuristic quantization systems, we
propose an emulation problem consistent with the neuromimetic paradigm. This
optimal quantization problem can be solved with model predictive control (MPC)
by deriving the conditions under which the quantized system can guarantee
(asymptotic) stability during emulation by optimizing a Lyapunov-like objective
function. The neuromimetic model features large numbers of discrete inputs, and
the optimization involves integer variables. The approach in the paper begins
by solving an optimization using model predictive control (MPC) and then using
a neural network to train the data generated in this process and applying
Fincke and Pohst's sphere decoding algorithm to narrow down the search for the
optimal solution.
