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Forecasting the productivity of a virtual enterprise by agent-based fuzzy collaborative intelligence-With Facebook as an example

机译：基于代理的模糊协作智能预测虚拟企业的生产力-以Facebook为例

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摘要

Since the Internet bubble, firms that focus on virtual enterprises have sought to enhance productivity. To achieve this goal, a firm must evaluate its present productivity and estimate its future productivity. To overcome the considerable uncertainty in estimates of productivity, we propose an agent-based fuzzy collaborative intelligence approach that predicts productivity. First, a fuzzy learning model is built and used to estimate future productivity. Subsequently, the fuzzy learning model is fitted by several agents with diverse settings; those agents produce different productivity forecasts. Fuzzy intersection is then applied to determine the narrowest range that contains the actual value from the fuzzy forecasts. Finally, a back-propagation network derives a representative value from the fuzzy forecasts. The real-world case of Facebook is used to demonstrate the applicability of the proposed methodology.
机译：自从互联网泡沫以来，专注于虚拟企业的公司一直在寻求提高生产力。为了实现这一目标，公司必须评估其当前的生产率并估计其未来的生产率。为了克服生产率估算中的巨大不确定性，我们提出了一种基于代理的模糊协作智能方法来预测生产率。首先，建立模糊学习模型，并将其用于估算未来的生产率。随后，模糊学习模型由具有不同设置的多个代理拟合。这些代理商产生不同的生产率预测。然后，应用模糊交集来确定包含来自模糊预测的实际值的最窄范围。最后，反向传播网络从模糊预测中得出代表值。 Facebook的实际案例用于证明所提出方法的适用性。

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