Deep Petri nets of unsupervised and supervised learning

• Published in: Measurement and control (London) 2020-08, Vol.53 (7-8), p.1267-1277
• Main Authors: Lin, Yi-Nan, Hsieh, Tsang-Yen, Yang, Cheng-Ying, Shen, Victor RL, Juang, Tony Tong-Ying, Chen, Wen-Hao
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; Deep learning; Machine learning; Neural networks; Parameters; Petri nets; Supervised learning
• ISSN: 0020-2940; 2051-8730
• DOI: 10.1177/0020294020923375

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.