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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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具有随机权重的回声状态网络的普遍性和近似性边界.

Zhen Li, Yunfei Yang

    IEEE transactions on neural networks and learning systems
    |December 13, 2023
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    概括

    具有随机内部权重的回声状态网络 (ESN) 可以统一近似连续的,时间不变的运算符. 这项研究证明了一般激活函数的普遍性,为ReLU激活提供了明确的构造.

    科学领域:

    • 机器学习 机器学习
    • 动态系统理论 动态系统理论
    • 计算神经科学是一种神经科学.

    背景情况:

    • 回声状态网络 (ESN) 是经常性的神经网络,越来越多地用于建模动态系统.
    • 已经观察到经验上的成功,特别是在优化读出权重和随机内部权重的情况下.
    • 之前的工作证明了ESN使用修正线性单位 (ReLU) 激活函数的普遍性.

    研究的目的:

    • 扩大对回声状态网络普遍性的理论理解,超越特定的激活功能.
    • 为构建普遍接近的ESN提供一个通用的框架.
    • 分析连续,因果,时间不变运算符的近似能力.

    主要方法:

    • 为回声国家网络开发替代建筑.
    • 在特定条件下的ESN具有一般激活函数的普遍性的数学证明.
    • 设计内部权重的采样程序,以实现统一的近似.
    • 对 ReLU 激活 ESN 的近似误差量化.

    主要成果:

    • 证明了使用随机生成的内部权重的回声状态网络可以高概率地统一近似任何连续的,因果的,时间不变的运算符.
    • 对于使用一般激活功能,不仅限于ReLU的ESN建立了普遍性.

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  • 为内部权重提供了明确的抽样程序,特别是对于ReLU激活的ESN.
  • 量化了使用构造的 ReLU ESN 的足够常规运营商的近似误差.
  • 结论:

    • 该研究证实并概括了回声状态网络在接近复杂动态系统方面的普遍性.
    • 这些发现为设计更通用和更强大的ESN模型提供了理论基础.
    • 明确的构造和错误量化为各种科学领域的实际应用铺平了道路.