Realising the promise of neural networks for practical optimisation: improving their efficiency and effectivess through chaotic dynamics and hardware implementation

组合优化问题，如运输路线和装配线调度是至关重要的许多行业的效率，但他们的组合爆炸使快速解决困难。神经网络（NN）有很大的潜力，快速解决方案，通过硬件实现，但我们需要提高其解决方案的质量之前，开发硬件。我们之前已经证明，丰富的混沌动力学可以提高神经网络的效率和有效性。我们的目标是开发新的混沌神经网络模型，严格评估它们在工业上的重要问题，如制造业，物流和电信中出现的问题，并通过硬件加速来展示它们的速度。

Combinatorial optimisation problems such as transportation routing and assembly-line scheduling are critical to the efficiency of many industries, but their combinatorial explosion makes rapid solution difficult. Neural networks (NNs) hold much potential for rapid solution though hardware implementation, but we need to improve the quality of their solutions before developing hardware. We have previously shown that the rich dynamics of chaos can improve the efficiency and effectiveness of NNs. We aim to develop new chaotic NN models, rigorously evaluate them on industrially significant problems such as those arising in manufacturing, logistics and telecommunications, and demonstrate their speed through hardware acceleration.