Failure and reliability predictions by infinite impulse response locally recurrent neural networks

Chemical Engineering Transactions

Volumn 26, Issue , 2012, Pages 117-122

  Zio, Enrico ^a,b   Broggi, Matteo ^c   Golea, Lucia R ^b   Pedroni, Nicola ^b  

^a ECOLE CENTRALE PARIS   (France)

^b POLITECNICO DI MILANO   (Italy)

^c UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS; FORECASTING; IIR FILTERS; IMPULSE RESPONSE; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RELIABILITY;

ARTIFICIAL NEURAL NETWORK MODELING; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; ENGINEERED COMPONENTS; INFINITE IMPULSE RESPONSE; MODELLING TECHNIQUES; MULTI LAYER PERCEPTRON; RADIAL BASIS FUNCTION(RBF); RELIABILITY PREDICTION;

RECURRENT NEURAL NETWORKS;

EID: 84864486999     PISSN: 22839216     EISSN: 22839216     Source Type: Journal    
DOI: 10.3303/CET1226020     Document Type: Conference Paper

References (18)

1. Amjady N., Ehsan M., 1999, Evaluation of power systems reliability by artificial neural network. IEEE Tr. in Power Sys. 14, 287-292.
2. Back A. D., Tsoi A.C., 1991, FIR and IIR synapses, a new neural network architecture for time series modelling, Neural Comput. 3, 375-385.
3. Box G. E. P., Jenkins G.M., 1976, Time Series Analysis-Forecasting and control. Holden-Day, Oakland, California, USA.
4. Cadini F., Zio E., Pedroni N., 2007, Simulating the Dynamics of the Neutron Flux in a Nuclear Reactor by Locally Recurrent Neural Networks, Annals of Nuclear Energy, 34(6), 483-495. (Pubitemid 46754661)
5. Campolucci P., Uncini A., Piazza F., Rao B.D., 1999, On-Line Learning Algorithms of Locally Recurrent Neural Networks, IEEE Trans. Neural Networks, 10, 253-271.
6. Cai K. Y., Cai L., Wang W.D., Yu Z.Y., Zhang D., 2001, On the neural network approach in software reliability modelling, The Journal of Systems and Software, 58, 47-62. (Pubitemid 32746266)
7. Charfield E., 1991, The Analysis of Time Series. Chapman and Hall, New York, New York, USA.
8. Ho S.L., Xie M., Goh T.N., 2002, A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction, Computers and Industrial Engineering, 42, 371-375. (Pubitemid 34410764)
9. Ho S.L., Xie M., Goh T.N., 2003, A study of the connectionist models for software reliability prediction, Computers and mathematics with applications, 46, 1037-1045.
10. Hu Q.P., Xie M., Ng S.H., Levitin G., 2007, Robust recurrent neural network modelling for software fault detection and correction prediction, Reliability Engineering and System Safety, 92, 332-340. (Pubitemid 44635168)
11. Hornik K., Stinchcombe M., White H., 1989, Multilayer Feedforward Networks are Universal Approximators, Neural Networks, 2, 359-366. (Pubitemid 20609008)
12. Liu M.C., Sastri T., Kuo W., 1995, An exploratory study of a neural network approach for reliability data analysis, Quality and Reliability Engineering International, 11, 107-112.
13. Mandic D., Chambers J., 2001, Recurrent Neural Networks for Prediction. John Wiley and Sons Ltd, New York, New York, USA.
14. Siewiorek D.P., Swarz R.S., 1982, The Theory and Practice of Reliable System Design. Digital Press, Bedford, Massachusetts, USA.
15. Su C.T., Tong L.I., Leou C.M., 1997, Combining time series and neural network approaches. Journal of the Chinese Institute of Industrial Engineers, 4, 419-430.
16. Tian L., Noore A., 2005a, Evolutionary neural network modelling for software cumulative failure time prediction, Reliability Engineering and System Safety, 87(1), 45-51.
17. Tian L., Noore A., 2005b, On-line prediction of software reliability using an evolutionary connectionist model, J. Systems Software, 77, 173-180. (Pubitemid 40612685)
18. Xu K., Xie M., Tang L.C., Ho S.L., 2003, Application of neural networks in forecasting engine systems reliability, Applied Soft Computing, 2, 255-268. (Pubitemid 40442502)