基于梯度的潮门优化

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Anil Radhakrishnan , Sudeshna Sinha , K. Murali , William L. Ditto
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引用次数: 0

摘要

我们提出了一种配置超门的方法,使用基于梯度的优化来复制标准布尔逻辑门的行为。通过定义潮门编码的可微公式,我们优化了其可调参数,以重新配置标准逻辑门功能的潮门。这种新颖的方法使我们能够将成熟的机器学习工具用于优化潮门,而不需要高参数计数神经网络的成本。我们进一步将这种方法扩展到同时优化多个门以调谐逻辑电路。实验结果证明了该技术在不同非线性系统和配置中的可行性,为非线性计算设备的参数自动发现提供了一条途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gradient based optimization of Chaogates
We present a method for configuring Chaogates to replicate standard Boolean logic gate behavior using gradient-based optimization. By defining a differentiable formulation of the Chaogate encoding, we optimize its tunable parameters to reconfigure the Chaogate for standard logic gate functions. This novel approach allows us to bring the well established tools of machine learning to optimizing Chaogates without the cost of high parameter count neural networks. We further extend this approach to the simultaneous optimization of multiple gates for tuning logic circuits. Experimental results demonstrate the viability of this technique across different nonlinear systems and configurations, offering a pathway to automate parameter discovery for nonlinear computational devices.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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