Faculty of Business Economics, Universiteit Hasselt, Belgium; Department of Cognitive Science & Artificial Intelligence, Tilburg University, The Netherlands.
Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland.
Neural Netw. 2020 Apr;124:258-268. doi: 10.1016/j.neunet.2020.01.019. Epub 2020 Jan 30.
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.
混合人工智能通过依赖人类知识和历史数据记录来构建智能系统。在本文中，我们从神经学的角度来处理这个问题，特别是在对动态系统进行建模和模拟时。首先，我们提出了一种模糊认知图架构，要求专家定义输入神经元之间的相互作用。作为第二项贡献，我们引入了一种快速且确定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，并可以在单个步骤中完成。此外，我们讨论了一种确定权重相关性的模型，这有助于我们更好地理解系统。最后但同样重要的是，我们引入了两种校准方法，以便在去除潜在多余权重后调整模型。
