Apiecionek Łukasz
Faculty of Computer Science, Kazimierz Wielki University in Bydgoszcz, Jana Karola Chodkiewicza 30, 85-064 Bydgoszcz, Poland.
Sensors (Basel). 2024 Aug 10;24(16):5169. doi: 10.3390/s24165169.
The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted.
本文所介绍研究的主要目标是引入一种人工神经网络，该网络所需的计算能力低于传统深度神经网络。这种人工神经网络（ANN）是通过应用有序模糊数（OFN）来实现的。在工业4.0的背景下，有许多应用场景可以使用该解决方案进行数据处理。它允许在小型设备的网络边缘部署人工智能，无需将大量数据传输到云服务器进行分析。未来，这样的网络将更容易在诸如物联网的小规模解决方案中实现。本文展示了对一个真实系统进行监测、检测和预测异常情况的测试结果。
