论文标题
部分可观测时空混沌系统的无模型预测
Tackling cyclicity in causal models with cross-sectional data using a partial least square approach. Implication for the sequential model on internet appropriation
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Working with SEM and crosssectional data, and depending on the studied phenomenon, assuming an acyclic model may mean that we obtain only a partial view of the mechanisms that explain causal relationships between a set of theoretical constructs, treated as antecedents and consequences. Our twogiven that variables are step approach allows researchers to identify and measure cyclic effects when working with cross algorithm. Using the resources and appropriation tsectional data and a PLS modelling heory and the sequential model of internet appropriation, w e demonstrate the importance of considering cyclic effects. Our results show that opportunities for physical access followed by digital skills acquisition enhance internet usage (acyclic effects), but also that internet usage intensity, in reverse, reinforces both digital skills and physical access (cyclic effects), supporting Norris (2001) social stratification hypothesis regarding future evolution of the digital divide.
