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非线性电振荡器网络的频率响应和间隙调谐。

Frequency response and gap tuning for nonlinear electrical oscillator networks.

机构信息

Applied Mathematics Unit, University of California Merced, Merced, California, United States of America.

出版信息

PLoS One. 2013 Nov 4;8(11):e78009. doi: 10.1371/journal.pone.0078009. eCollection 2013.

Abstract

We study nonlinear electrical oscillator networks, the smallest example of which consists of a voltage-dependent capacitor, an inductor, and a resistor driven by a pure tone source. By allowing the network topology to be that of any connected graph, such circuits generalize spatially discrete nonlinear transmission lines/lattices that have proven useful in high-frequency analog devices. For such networks, we develop two algorithms to compute the steady-state response when a subset of nodes are driven at the same fixed frequency. The algorithms we devise are orders of magnitude more accurate and efficient than stepping towards the steady-state using a standard numerical integrator. We seek to enhance a given network's nonlinear behavior by altering the eigenvalues of the graph Laplacian, i.e., the resonances of the linearized system. We develop a Newton-type method that solves for the network inductances such that the graph Laplacian achieves a desired set of eigenvalues; this method enables one to move the eigenvalues while keeping the network topology fixed. Running numerical experiments using three different random graph models, we show that shrinking the gap between the graph Laplacian's first two eigenvalues dramatically improves a network's ability to (i) transfer energy to higher harmonics, and (ii) generate large-amplitude signals. Our results shed light on the relationship between a network's structure, encoded by the graph Laplacian, and its function, defined in this case by the presence of strongly nonlinear effects in the frequency response.

摘要

我们研究非线性电振荡器网络,其最小示例由一个电压相关电容、一个电感器和一个由纯音源驱动的电阻器组成。通过允许网络拓扑是任何连通图的拓扑,这样的电路概括了在高频模拟设备中证明有用的空间离散非线性传输线/晶格。对于这样的网络,我们开发了两种算法来计算当子集节点以相同的固定频率驱动时的稳态响应。我们设计的算法比使用标准数值积分器逐步达到稳态的算法精确和高效几个数量级。我们通过改变图拉普拉斯矩阵(即线性系统的共振)的特征值来增强给定网络的非线性行为。我们开发了一种牛顿型方法来求解网络电感,以使图拉普拉斯矩阵实现期望的一组特征值;这种方法可以在保持网络拓扑固定的同时移动特征值。通过使用三种不同的随机图模型运行数值实验,我们表明缩小图拉普拉斯矩阵的前两个特征值之间的差距可以极大地提高网络(i)将能量转移到更高谐波和(ii)产生大振幅信号的能力。我们的结果揭示了网络的结构(由图拉普拉斯矩阵编码)与其功能之间的关系,在这种情况下,功能由频率响应中存在的强非线性效应定义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d0/3817173/0b510189d2d4/pone.0078009.g001.jpg

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