A novel hybrid intelligent fault diagnosis method based on improved RBF neural network

摘要：

　　The radial basis function neural network (RBFNN) is a great potential artificial intelligence technology and can effectively realize the fault diagnosis for small sample and nonlinear problem.But the parameters of RBFNN model seriously affects the generalization ability and diagnosis accuracy on the great extent.So an improved differential evolution algorithm based on dynamic adaptive adjustment strategy is proposed to optimize the parameters of RBFNN model for obtaining the optimal RBFNN(DASDERBFNN) method.Then the proposed DASDERBFNN method is used to construct a new fault diagnosis (DSDRBFNFD) method.The experiment results show that the proposed DSDRBFNFD method can obtain the higher accuracy of fault diagnosis and is effective fault diagnosis for the engine.

关键词： differential evolution RBF neural network intelligent fault diagnosis

作者: Liu Yi

作者单位: School of Mechanical and Electronic Engineering Wuhan Donghu University,Wuhan,Hubei,430212,China

会议类型: 国际会议

会议名称: 2016 International Conference on Economics and Management Innovations(2016经济与管理创新国际会议)

会议地点: 武汉

会议语种:英文

页码: 207-211

在线出版日期: 2016-07-09（万方平台首次上网日期，不代表论文的发表时间）