Meesad P, Yen G G
Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater 74078, USA.
ISA Trans. 2000;39(3):293-308. doi: 10.1016/s0019-0578(00)00027-6.
An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.
本文提出了一种创新的神经模糊网络用于模式分类应用，特别是振动监测。模糊集解释被纳入网络设计以处理不精确信息。神经网络架构用于基于混合监督学习方案自动推导模糊if-then规则。所提出的神经模糊分类器配备了一种单遍、在线和增量学习算法。该网络可被视为一种自组织分类器，具有在不遗忘旧知识的情况下自适应学习新信息的能力。所提出的神经模糊网络的分类性能在费希尔鸢尾花数据集上得到验证，该数据集是一个著名的基准数据集。对于泛化能力，神经模糊网络可实现97.33%的正确分类。此外，为了证明所提出的神经模糊范式的效率和有效性，使用韦斯特兰数据集进行了数值模拟。韦斯特兰数据集由从美国海军CH-46E直升机试验台收集的振动数据组成。通过使用简单的快速傅里叶变换技术进行特征提取，所提出的神经模糊网络显示出了有前景的结果。使用各种扭矩水平进行训练和测试，该网络实现了100%的正确分类。
