Application Research on Fault Diagnosis Based on Improved Wavelet Neural Network
Aluminum electrolysis is a nonlinear,multi-couplings,time-variable and large time-delay industrial process system.The paper puts forward the fault diagnoisis method of improved wavelet Elman neural network,which firstly simplifies the input of network with the method of principal component analysis,secondly,the weights,as well as scale factor and shift factor of the wavelet function are optimized by use of the wavelet Elman network which is optimized by improved particle swarm algorithm.Then it is verified by the simulation.The simulation results show that the method can precisely forecast the aluminium electrolysis equipment faults and improve the production and quality of aluminum.
aluminum electrolysis faults principal component analysis improved particle swarm algorithm wavelet Elman neural network optimization
Jiejia LI Jie LI Rui QU Ying LI
School of Information and Control Engineering,Shenyang Jianzhu Uninersity, Shenyang China,110168 Shenyang Academy of Instrumentation Science Shenyang China,110043
国际会议
杭州
英文
268-272
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）