# Model Predictive Control for Neuromimetic Quantized Systems

arXiv: https://arxiv.org/abs/2212.09887 · HTML: https://mlsanity.com/paper/2212.09887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [Emulation Learning for Neuromimetic Systems](https://mlsanity.com/paper/2305.03196/) (2023)

## Abstract (arXiv)

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.