Software aging prediction model based on fuzzy wavelet network with adaptive genetic algorithm

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2006, Pages 659-666

  Ning, Meng Hai ^a   Yong, Qi ^a   Di, Hou ^a   Ying, Chen ^b   Zhong, Zhao Ji ^a  

^a XI'AN UNIVERSITY   (China)

^b IBM CHINA RESEARCH LAB   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; FUZZY INFERENCE; FUZZY RULES; GENETIC ALGORITHMS; SOFTWARE RELIABILITY; TIME SERIES ANALYSIS;

DATA SERIES; FUZZY WAVELET NETWORK; PERFORMANCE PARAMETERS; RESOURCE CONSUMPTION; SOFTWARE AGING;

COMPUTER SOFTWARE SELECTION AND EVALUATION;

EID: 38949084869     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2006.104     Document Type: Conference Paper

References (18)

1. S. Garg, A. Puliafito, M. Telek, and K.S. Trivedi, "A Methodology for Detection and Estimation of Software Aging", Intl. Symp. On Software Reliability Engineering, ISSRE, Nov. 1998.
2. Y. Huang, C. Kintala, N. Kolettis, and N. Fulton, "Software Rejuvenation: Analysis, Module and Applications", IEEE Intl. Symposium on Fault-Tolerant Computing, FTCS 25, pp. 381-390, June 1995.
3. R. Chillarege, S. Biyani, and J. Rosenthal. "Measurement of failure rate in widely distributed software", In Proc. of 25th IEEE Intl. Symp. on Fault-Tolerant Computing, Pasadena, CA, pp. 424-433, July 1995.
4. D. Tang, and R.K. Iyer. "Dependability measurement modeling of a multicomputer system", IEEE Trans. on Computers, vol. 42, no. 1, pp. 62-75, January 1993.
5. T.Y. Lin, and D.P. Siewiorek, "Error log analysis: Statistical modeling and heuristic trend analysis", IEEE Trans. on Reliability, vol. 39, no. 4, pp. 419-432, October 1990.
6. B. Amir, and Geva, "Scalenet-Multiscale Neural network Architecture For Time Series Prediction", IEEE Trans. on Neural Networks, vol.9, no.5, pp. 1471-1482, 1998.
7. Rattan, S.P. Sanjay, Hsieh, and W. William, "Complexvalued neural networks for nonlinear complex principal component analysis", Neural Networks, vol. 18, no. 1, pp. 61-69, January 2005.
8. Q. Zhang, and A. Benvenise, "Wavelet network", IEEE Trans. on Neural Network, vol. 3, no. 6, pp. 889-898, 1992.
9. S.T. Li, and S.C. Chen, "Function Approximation Using Robust Wavelet Neural Networks", The 14th IEEE International Conference on Tools with Artificial Intelligence, Taiwan, pp. 483-488. Nov. 2002.
10. Z. Bashir, and M.E. El-Hawary, "Short Term Load Forecasting By Using Wavelet Neural Networks", The IEEE Conference on Electrical and Computer Engineering, Canadian, March, pp. 163-166, 2000.
11. D.W.C. Ho, P. Zhang, and J. Xu, "Fuzzy wavelet networks for function learning", IEEE Trans. on Fuzzy Systems, vol. 9, no. 1, pp. 200-211, 2001.
12. F. Alonge, F. D'Ippolito, S. Mantione, and F.M. Raimondi, "A new method for optimal synthesis of wavelet-based neural networks suitable for identification purposes," In Proc. 14th IFAC, Beijing, P.R. China, pp. 445-450, June 1999.
13. X.G. Zhang, S.J. Ren, J.H. Xu, and Z.C. Zhu, "Robust Multiwavelets Support Vector Regression Network", Intl. Conference on Control and Automation, Budapest, Hungary, pp. 27-29, June 2005.
14. L.X. Wang, and J.M. Mendel, "Fuzzy basis function, universal approximation and orthogonal least-squares learning," IEEE Trans. Neural Networks, vol. 3, pp. 807-814, 1992.
15. T.Takagi, and M.Sugeno, "Fuzzy identification of systems and ist application to modeling and control". IEEE Trans. on Systems, Man, Cybernetics, pp. 116-132, 1985.
16. M.Sugeno, and G.T.Kang, "Structure identification of fuzzy models". Fuzzy Sets and Systems, pp. 15-33, 1988.
17. A. Prochizka, and S. Vladimir "Time Series Prediction Using Genetically Trained Wavelet Networks", Sys, Vladimir Source: Neural Networks for Signal Processing -Proceedings of the IEEE Workshop, pp. 195-203, 1994.
18. M. Srinivas, and L.M. Patnaik, "Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms", IEEE Trans. on Systems, Man and Cybernetics, vol. 24, no. 4, April 1994.