Study on Fault-Diagnosis Models of Different Neural Networks and Ensemble

摘要：

Different diagnosis models, including multiplayer perceptron (MLP), radial basis function (RBF) and two types of support vector machines (SVMs), were designed, analyzed and compared based on the fault diagnosis of an analogue circuit instance. The experimental results show SVM model is of higher classification rate than MLP and RBF models, while MLP model has better ability to deal with uncertain signals. Considering different models correspond to different strategies, we combine four models of MLP, RBF and two SVMs to combine a diagnosis ensemble, which can achieve more accurate results than any individual model in the ensemble. The ensemble technique can provide a theoretical basis for further study on the fault diagnosis of analogue circuits.

关键词： multilayer perceptron radial basis function support vector machine neural network ensemble analogue circuits fault diagnosis

作者: Kim An

作者单位: National Key Laboratory of Science and Technology on Electronic Test and Measurement North University of China Taiyuan, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 225-229

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）