利用混沌动力学作为深度神经网络
Exploiting Chaotic Dynamics as Deep Neural Networks
AI总结：
该研究揭示混沌本质存在于多种深度神经网络中，并提出直接利用混沌动力学构建深度学习架构的新方法，在多个混沌系统上取得优于传统网络的准确率、收敛速度与效率，为混沌与机器学习和神经形态计算的融合开辟了新路径。
摘要：
混沌源于非线性与对初始状态的敏感性，呈现出复杂的动力学行为。这些特性表明其具有深层的表达能力，凸显了其在高级计算应用中的潜力。然而，有效利用混沌动力学进行信息处理的策略在很大程度上仍然难以实现。在本研究中，我们揭示了混沌的本质可以在多种最先进的深度神经网络中找到。受这一发现的启发，我们提出了一种直接利用混沌动力学构建深度学习架构的新方法。我们的方法在多个不同的混沌系统上进行了系统性评估。在所有实例中，我们的框架在准确率、收敛速度和效率方面均优于传统深度神经网络。此外，我们发现瞬态混沌的形成在我们的方案中发挥了积极作用。总体而言，本研究为混沌在信息处理中的整合提供了一条新路径，而混沌长期以来在该领域一直被忽视，并为混沌动力学在机器学习和神经形态计算领域中的前瞻性融合提供了见解。
英文摘要：
Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potential for advanced computational applications. However, strategies to effectively exploit chaotic dynamics for information processing have largely remained elusive. In this study, we reveal that the essence of chaos can be found in various state-of-the-art deep neural networks. Drawing inspiration from this revelation, we propose a novel method that directly leverages chaotic dynamics for deep learning architectures. Our approach is systematically evaluated across distinct chaotic systems. In all instances, our framework presents superior results to conventional deep neural networks in terms of accuracy, convergence speed, and efficiency. Furthermore, we found an active role of transient chaos formation in our scheme. Collectively, this study offers a new path for the integration of chaos, which has long been overlooked in information processing, and provides insights into the prospective fusion of chaotic dynamics within the domains of machine learning and neuromorphic computation.
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