论文标题
神经模拟量化系统的模型预测控制
Model Predictive Control for Neuromimetic Quantized Systems
论文作者
论文摘要
基于我们最近对神经启发式量化系统的研究，我们提出了与神经模仿范式一致的仿真问题。通过模型预测控制（MPC），可以通过优化类似lyapunov的目标函数来确保仿真过程中可以保证（渐近）稳定性的条件来解决此最佳量化问题。神经模型模型具有大量离散输入，并且优化涉及整数变量。本文中的方法首先使用模型预测控制（MPC）求解优化，然后使用神经网络来训练在此过程中生成的数据，并应用Fincke和Pohst的Sphere Decodododododododododododododododode算法以缩小搜索最佳解决方案的搜索。
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.
