用神经网络模拟模糊认知图，实现因果推理与可解释决策
Looking Through Glass Box
- 构建神经网络模拟模糊认知图的因果传播机制
- 采用Langevin动力学避免过拟合，反向求解输出节点值
- 提供可解释的修改依据，适用于个性化推荐场景
本文提出一种神经网络实现模糊认知图（FHM）的方法，并进行了相应评估。首先设计了一个神经网络，其行为与模糊认知图（FCM）一致：接收多个模糊认知图作为输入，并通过传播学习因果模式。该网络采用Langevin微分动力学，以避免过拟合，能够根据特定策略反向求解输出节点值。获得反向解后，用户可据此制定修改准则，从而判断不同服务或产品是否更适配需求。最后，在多个数据集上对网络性能进行了评估。
原文摘要 · Abstract (English)
This essay is about a neural implementation of the fuzzy cognitive map, the FHM, and corresponding evaluations. Firstly, a neural net has been designed to behave the same way that an FCM does; as inputs it accepts many fuzzy cognitive maps and propagates them in order to learn causality patterns. Moreover, the network uses langevin differential Dynamics, which avoid overfit, to inverse solve the output node values according to some policy. Nevertheless, having obtained an inverse solution provides the user a modification criterion. Having the modification criterion suggests that information is now according to discretion as a different service or product is a better fit. Lastly, evaluation has been done on several data sets in order to examine the networks performance.
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