Hybrid artificial intelligence deals with the construction of intelligent systems by relying on both human knowledge and historical data records. In this paper, we approach this problem from a neural perspective, particularly when modeling and simulating dynamic systems. Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to define the interaction among the input neurons. As a second contribution, we introduce a fast and deterministic learning rule to compute the weights among input and output neurons. This parameterless learning method is based on the Moore-Penrose inverse and it can be performed in a single step. In addition, we discuss a model to determine the relevance of weights, which allows us to better understand the system. Last but not least, we introduce two calibration methods to adjust the model after the removal of potentially superfluous weights.

中文翻译：

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混合模糊认知图的确定性学习和网络约简方法。

混合人工智能通过依靠人类知识和历史数据记录来处理智能系统的构建。在本文中，我们从神经的角度解决了这个问题，特别是在对动态系统进行建模和仿真时。首先，我们提出了一种模糊认知图架构，其中要求专家定义输入神经元之间的交互。作为第二个贡献，我们引入了一种快速确定性的学习规则来计算输入和输出神经元之间的权重。这种无参数学习方法基于Moore-Penrose逆，并且可以在单个步骤中执行。此外，我们讨论了确定权重相关性的模型，这使我们可以更好地理解系统。最后但并非最不重要的，