Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network. But there are two shortcomings in neural network. First, it is not easy to be understood. When encountering the needs for implementation, it often requires a lot of relevant research efforts to implement the neural network. Second, the structure is complex. When constructing a perfect learning structure, in order to achieve the fully defined connection between nodes, the overall structure becomes complicated. It is hard for developers to track the parameter changes inside. Therefore, the goal of this article is to provide a more streamlined method so as to perform deep learning. A modified high-level fuzzy Petri net, called deep Petri net, is used to perform deep learning, in an attempt to propose a simple and easy structure and to track parameter changes, with faster speed than the deep neural network. The experimental results have shown that the deep Petri net performs better than the deep neural network.

中文翻译：

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无监督和监督学习的深度 Petri 网

人工智能是计算机科学中最热门的研究课题之一。一般来说，当涉及到进行深度学习的需求时，最直观、最独特的实现方式就是使用神经网络。但是神经网络有两个缺点。首先，不容易理解。当遇到实现的需求时，往往需要大量的相关研究工作来实现神经网络。二是结构复杂。在构建完美的学习结构时，为了实现节点之间完全定义的连接，整体结构变得复杂。开发人员很难跟踪内部的参数变化。因此，本文的目标是提供一种更精简的方法来进行深度学习。一种改进的高级模糊 Petri 网，称为深度 Petri 网，用于执行深度学习，试图提出一种简单易行的结构并跟踪参数变化，速度比深度神经网络更快。实验结果表明，深度 Petri 网络的性能优于深度神经网络。