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.

翻译：本文探讨了模糊认知图（FCM）的一种神经实现方法——FHM模型及其相应的评估。首先，我们设计了一个神经网络，使其行为方式与模糊认知图一致；该网络接收多个模糊认知图作为输入，并通过传播学习因果关系模式。此外，网络采用朗之万微分动力学以避免过拟合，并根据特定策略反向求解输出节点值。然而，获得反向解为用户提供了修改标准。拥有修改标准意味着信息现在可根据判断进行调整，以更好地适应不同的服务或产品需求。最后，我们在多个数据集上进行了评估，以检验网络的性能。