Abstract
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.
Highlights
Encountering the needs for implementation, it often requires lot of relevant efforts to construct a learning structure
In order to make a full connection among nodes, the learning structure becomes complicated
This study proposes a concept of ‘‘ring.’’ The deep Petri network (DPN) model was developed to simulate an neural network (NN) model.[14,15]
Summary
Encountering the needs for implementation, it often requires lot of relevant efforts to construct a learning structure. In order to make a full connection among nodes, the learning structure becomes complicated. It is not easy for developers to track the parameters changed inside. This reason motivates us to provide a streamlined method to perform deep learning. The HLFPN model provides a faster, less-complex, and easier implementation method. This article aims to use the multilayer HLFPN to perform deep learning, in an attempt to propose a simpler structure, easier to track the parameters changed, and a faster architecture than the neural network (NN).[2,3,4,5,6]
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