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一个修剪的前神经网络 (pruned-FNN) 方法来测量空气污染暴露

Xi Gong1, Lin Liu2, Yanhong Huang3

  • 1Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA. xigong@unm.edu.

Environmental monitoring and assessment
|September 11, 2023
PubMed
概括

一个新的修剪前神经网络 (pruned-FNN) 准确地估计了空气污染的暴露. 这种方法为环境流行病学研究提供了准确性,可用性和效率的平衡.

关键词:
空气污染 大气污染风险投资评估 风险投资评估地理信息系统 (GIS) 是一个机器学习 机器学习修剪后的前神经网络 (修剪后的FNN)空间建模 空间建模

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Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...