Vlamou Elena, Papadopoulos Basil
Department of Civil Engineering, Democritus University of Thrace, Xanthi, 67100, Greece.
AIMS Neurosci. 2019 Oct 22;6(4):266-272. doi: 10.3934/Neuroscience.2019.4.266. eCollection 2019.
The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a "Neuro-Fuzzy" system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.
人工神经网络与模糊逻辑系统的结合,通过创建智能自适应系统来表示现实世界中的问题。通过调整层与层之间的连接,人工神经网络能够进行学习。模糊逻辑推理系统提供了一种基于模糊集、规则以及模糊推理概念的计算框架。上述自适应结构的融合被称为“神经模糊”系统。在本文中,将对上述结构的主要元素进行研究。研究人员已经注意到这种融合可应用于医学应用中的模式识别。