A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectivel...A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.展开更多
The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element meth...The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.展开更多
Connection Admission Control(CAC)in ATM networks is the set o/actions taken by the networkto decide whether to accept connection requests during the phase of call establishment or call re-negotiation.CAC is an integra...Connection Admission Control(CAC)in ATM networks is the set o/actions taken by the networkto decide whether to accept connection requests during the phase of call establishment or call re-negotiation.CAC is an integral part of the preventive congestion control in ATM networks whose aim is to ensurenetwork performance.The CAC algorithm has the characteristics of the multitude of control parameters,high degree of computation complexity and strong time restrictions.In this paper we present a CACmechanism featured by combination of foreground control and background learning which is based onneural networks having the capabilities of self-learning and high-Speed processing.A case study is given,after which we discuss the practicability of the proposed algorithm.展开更多
基金Sci-Tech Planning Projects of Chongqing City,China(No.CSTC2007AA7003).
文摘A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.
基金the National Natural Science Foundation of China (Nos.19872002 and 10272003)Climbing Foundation of Northern Jiaotong University
文摘The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.
文摘Connection Admission Control(CAC)in ATM networks is the set o/actions taken by the networkto decide whether to accept connection requests during the phase of call establishment or call re-negotiation.CAC is an integral part of the preventive congestion control in ATM networks whose aim is to ensurenetwork performance.The CAC algorithm has the characteristics of the multitude of control parameters,high degree of computation complexity and strong time restrictions.In this paper we present a CACmechanism featured by combination of foreground control and background learning which is based onneural networks having the capabilities of self-learning and high-Speed processing.A case study is given,after which we discuss the practicability of the proposed algorithm.
