Annealing robust fuzzy neural networks for modeling of molecular autoregulatory feedback loop systems

International Journal of Fuzzy Systems

Volumn 10, Issue 1, 2008, Pages 11-17

  Chuang, Chen Chia ^a  

^a NATIONAL ILAN UNIVERSITY   (Taiwan)

Author keywords

Annealing robust fuzzy neural networks; Annealing robust learning algorithm; Molecular autoregulatory feedback loop systems; Outliers; Support vector regression

Indexed keywords

FEEDBACK; LEARNING ALGORITHMS; QUADRATIC PROGRAMMING; SUPPORT VECTOR MACHINES;

ANNEALING ROBUST FUZZY NEURAL NETWORKS; ANNEALING ROBUST LEARNING ALGORITHM; MOLECULAR AUTOREGULATORY FEEDBACK LOOP SYSTEMS; SUPPORT VECTOR REGRESSION (SVR);

FUZZY NEURAL NETWORKS;

EID: 42349108840     PISSN: 15622479     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. H. H. McAdams and A. Arkin, "Stochastic mechanisms in gene expression." Proc Natl. Acad. Sci. USA, 94:814-819, 1997.
2. H. Schmidt and E.W. Jacobsen, "Identifying feedback mechanisms behind complex cell behavior," IEEE Control Systems Magazine, pp. 91-102, 2004.
3. P. D'Haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, "Linear modeling of mRNA expression levels during CNS development and injury," Pacific Symp. Biocomputing, pp. 41-52, 1999.
4. H. D. Jong, "Modeling and simulation of genetic regulatory systems: a literature review," Journal of Computational Biology, vol. 9, pp. 67-103, 2002.
5. E. Andrianantoandro, S. Basu, D. K. Karig and R. Weiss, "Synthetic biology: new engineering rules for an emerging discipline," Molecular Systems Biology, msb4100073-E 1-14, 2006.
6. S. Hooshangi, S. Thiberge, R. Weiss, "Ultrasensitivity and noise propagation in a synthetic transcriptional cascade," Proc. Natl. Acad Sci. USA, pp. 3581-3586, 2005.
7. N. Rosenfeld, J.W. Young, U. Alon, P.S. Swain and M.B. Elowitz, "Gene regulation at the signal-cell level," Science, vol. 307, pp. 1962-1965, 2005.
8. J. T. Jeng and C. C. Chuang, "Selection of Initial Structures with Support Vector Regression for Fuzzy Neural Networks," Internatioal Journal of Fuzzy Systems, vol. 6, no. 2, pp. 63-70, 2004.
9. V. Vapnik, S. Golowich and A. J. Smola, "Support vector method for function approximation, regression estimation, and signal processing," Neural Information Processing Systems, 1997.
10. A. J. Smola and B. Schölkopf, "From regularization operators to support vector kernels", Neural Information Processing Systems, vol. 9. MIT Press, 1997.
11. J. T. Jeng, "Hybrid Approach of Selecting Hyper-Parameters of Support Vector Machine for Regression," IEEE Trans. on Systems, Man, and Cybernatics Part B:Cybernetics, vol. 36, no. 3, pp. 699-709, 2006.
12. J. T. Jeng, "Annealing Robust Fuzzy Basis Function for Modeling with Noise and Outliers," International Journal of Computer Applications in Technology, vol. 27, nos. 2/3, pp. 166-172, 2006.
13. L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice Hall, 1994.
14. C. C. Chuang, S. F. Su and C. C. Hsiao, " The Annealing Robust Backpropagation (BP) Learning Algorithm," IEEE Trans. Neural Networks, vol. 11, no. 5, pp. 1067-1077, 2000.
15. T.O. Scheper, D. Klinkenberg, J. V. Pelt and C. Pennartz, "A model of molecular circadian clocks: multiple mechanisms for phase shifting and a requirement for strong nonlinear interactions," Journal of Biological Rhythms, vol. 14, pp. 213-220, 1999.