Unsupervised and Supervised Deep Learning by Using Petri Nets

• Main Authors: Chen, Wen-Hao, 陳文皓
• Other Authors: Tong-Ying Tony Juan
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/87npgk

碩士 === 國立臺北大學 === 資訊工程學系 === 107 === In recent years, artificial intelligence (AI) is one of the hottest research topics in computer science. In general, when it comes to the needs to use deep learning, the most intuitive and unique implementation method is to use neural networks (NN), but there are many shortcomings in NN. First, it is not easy to understand. When encountering the needs for implementation, it often requires a lot of relevant research efforts to start the implementation. Second, the structure is complex. When building a perfect learning structure, in order to achieve the fully defined connection between the nodes, the overall structure becomes very complicated. It is not easy for developers to track the parameter changes inside. Therefore, the goal of this thesis is to provide a more streamlined method to perform deep learning. High level fuzzy Petri nets (HLFPN) are used to achieve deep learning, also called deep learning Petri nets (DLPN), in an attempt to propose a simple and easy structure, to track parameter changes, with speed faster than the deep learning neural network (DLNN). DLPN approach is to solve such a problem. First, write the predicate logic of the problem, then draw the structure according to predicate logic, use this drawn structure to perform targeted deep learning, because the design process makes the problem formulation have the advantages of the structure. DLPN makes the structure simpler, does not require redundant nodes to increase the complexity of the structure, and the simpler structure can also effectively improve the computing speed. The experimental results have shown that the DLPN performs better than the DLNN.