1 |
M. Mochizuki, Control of welding residual stress for ensuring integrity against fatigue and stress-corrosion cracking, Nucl. Eng. Des. 237 (2007) 107-123.
DOI
|
2 |
M.G. Na, J.W. Kim, D.H. Lim, Prediction of residual stress for dissimilar metals welding at NPPs using fuzzy neural network models, Nucl. Eng. Technol. 39 (2007) 337-348.
DOI
|
3 |
M.G. Na, J.W. Kim, D.H. Lim, Y.J. Kang, Residual stress prediction of dissimilar metals welding at NPPs using support vector regression, Nucl. Eng. Des. 238 (2008) 1503-1510.
DOI
|
4 |
D.H. Lim, I.H. Bae, M.G. Na, J.W. Kim, Prediction of residual stress in the welding zone of dissimilar metals using data-based models and uncertainty analysis, Nucl. Eng. Des. 240 (2010) 2555-2564.
DOI
|
5 |
P. Michaleris, J. Dantzig, D. Tortorelli, Minimization of welding residual stress and distortion in large structures, Weld. J. 78 (1999) 361s-366s.
|
6 |
A. Kulkarni, V.K. Jayaraman, B.D. Kulkarni, Control of chaotic dynamical systems using support vector machines, Phys. Lett. A 317 (2003) 429-435.
DOI
|
7 |
M.G. Na, J.W. Kim, I.J. Hwang, Collapse moment estimation by support vector machines for wall-thinned pipe bends and elbows, Nucl. Eng. Des. 237 (2007) 451-459.
DOI
|
8 |
V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
|
9 |
P.-F. Pai, W.-C. Hong, Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Convers. Manag. 46 (2005) 2669-2688.
DOI
|
10 |
W. Yan, H. Shao, X. Wang, Soft sensing modeling based on support vector machine and Bayesian model selection, Comput. Chem. Eng. 28 (2004) 1489-1498.
DOI
|
11 |
V. Kecman, Learning and Soft Computing, MIT Press, Cambridge, MA, 2001.
|
12 |
H.P. Graf, E. Cosatto, L. Bottou, I. Durdanovic, V. Vapnik, Parallel support vector machines: the cascade SVM, Adv. Neural Inf. Process. Syst. 17 (2005) 521-528.
|
13 |
G.P. Choi, D.Y. Kim, K.H. Yoo, M.G. Na, Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks, Nucl. Eng. Des. 300 (2016) 393-402.
DOI
|
14 |
S.L. Chiu, Fuzzy model identification based on cluster estimation, J. Intell. Fuzzy Syst. 2 (1994) 267-278.
DOI
|
15 |
I.V. Tetko, D.J. Livingstone, A.I. Luik, Neural network studies, 1. Comparison of overfitting and overtraining, J. Chem. Inf. Comput. Sci. 35 (1995) 826-833.
DOI
|
16 |
Hibbitt, Karlson & Sorensen, Inc., ABAQUS/Standard User's Manual, Hibbitt, Karlson & Sorensen, Inc., Providence, Rhode Island, U.S.A, 2001.
|