An optimised product-unit neural network with a novel PSO-BP hybrid training algorithm: Applications to load-deformation analysis of axially loaded piles

Engineering Applications of Artificial Intelligence

Volumn 26, Issue 10, 2013, Pages 2305-2314

  Ismail, A ^b   Jeng, D S ^a,b,c   Zhang, L L ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY OF DUNDEE   (United Kingdom)

^c GRIFFITH UNIVERSITY   (Australia)

Author keywords

Hybrid training; Neural network; Piled foundation; Product unit neural network

Indexed keywords

GLOBAL SEARCH CAPABILITY; HYBRID LEARNING ALGORITHM; HYBRID TRAINING; LOAD-DEFORMATION CURVES; NEURAL NETWORK TRAINING; PARTICLE SWARM OPTIMISATION; PILED FOUNDATIONS; PRODUCT-UNIT;

BACKPROPAGATION ALGORITHMS; COMPLEX NETWORKS; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO);

PILES;

EID: 84887015290     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2013.04.007     Document Type: Article

References (26)

1. Abdelbar, A.G.; Tagliarini, G.; 1996. Honest: a new high-order feed forward neural network. In: Proceedings of the IEEE International Conference on Neural Network, vol. 2, pp. 1257-1262.
2. W.T. Chan, Y.K. Chow, and L.F. Liu Neural network an alternative to pile driving formulas Comput. Geotech. 17 1995 135 156
3. M. Clere, and J. Kennedy The particle swarm - explosion, stability and convergence in a multidimensional complex space IEEE Trans. Evol. Comput. 6 1 2002 58 73
4. H.M. Coyle, and L.C. Reese Land transfer for axially loaded piles in clay J. Soil Mech. Found. Div.; ASCE 92 2 1966 1 26
5. Y. Cui, S.G. Ju, F. Han, and T.Y. Gu An improved approach combining random PSO with BP for feedforward neural networks Artif. Intell. Comput. Intell. 5855 2009 361 368
6. C.S. Desai Numerical design-analysis for piles in sands J. Geotech. Eng. Div.; ASCE 100 6 1974 613 635
7. R. Durbin, and R. Rumelhart Product units a computationally powerful and biologically plausible extension to backpropagation networks Neural Comput. 1 1989 133 142
8. Eberhart, R.C.; Kennedy, J.; 1995. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro-Machine and Human Science, pp. 39-43.
9. E. Elbeltagi, T. Hegazy, and D. Grierson Comparison among five evolutionary-based optimization algorithms Adv. Eng. Inf. 19 2005 43 53
10. W.G.K. Fleming A new method of single pile settlement and analysis Géotechnique 42 3 1992 411 425
11. W.G.K. Fleming, A.J. Weltman, M.F. Randolph, and W.K. Elson Piling Engineering 3rd ed. 2009 Taylor and Francis
12. A.T.C. Goh Pile driving records reanalyzed using neural networks J. Geotech. Eng.; ASCE 122 6 1996 492 495
13. Gudise, V.G.; Venayagamoorthy, G.K.; 2003. Comparison of particle swarm optimization and back-propagation as training algorithms for neural networks. In: Proceedings of the IEEE Swarm Intelligence Symposium, vol. 2, pp. 110-117.
14. M.T. Hagan, and M.B. Menhaj Training feedforward networks with the Marquardt algorithm IEEE Trans. Neural Network 5 1994 989 993
15. Ismail, A.; Engelbrecht, A.P.; 2000. Global optimization algorithms for training product-unit neural networks. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 1, Como, Italy, pp. 132-137.
16. A. Ismail, and D.-S. Jeng Modelling load settlement behaviour of piles using high order neural network (HON-PILE model) Eng. Appl. Artif. Intell. 24 5 2011 813 821
17. Kennedy, J.; Eberhart, R.C.; 1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948.
18. I.M. Lee, and J.H. Lee Prediction of pile bearing capacity using artificial neural networks Comput. Geotech. 18 3 1996 189 200
19. D. Marquardt An algorithm for least-squares estimation of nonlinear parameters SIAM J. Appl. Math. 11 1963 431 441
20. McVay, M.C.; Towsend, F.C.; Bloomquist, D.G.; O'Brien, M.O.; Caliendo, J.A.; 1989. Numerical analysis of vertically loaded pile groups. In: Proceedings of the Foundation Engineering Congress, Illinois, pp. 675-690.
21. Nawari, N.O.; Liang, R.; Nusairat, J.; 1999. Artificial intelligence techniques for the design and analysis of deep foundations. Electron. J. Geotech. Eng. 〈 http://www.ejge.com/1999/Ppr9909/Abs9909.htm âŒ.
22. P.F. Nejad, M.B. Jaksa, M. Kakhi, and B.A. McCabe Prediction of pile settlement using artificial neural networks based on standard penetration test data Comput. Geotech. 36 2009 1125 1133
23. H.G. Poulos, and E.H. Davis Pile Foundation Analysis and Design 1980 Wiley
24. Ren, Z.F.Z.; Yu, D.; Yan, Y.; 2005. Application of an improved particle swarm optimization algorithm for neural network training. In: International Conference on Neural Networks and Brain, vol. 3, pp. 1693-1698.
25. D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning representations by back-propagating errors Nature 323 1986 533 536
26. J.R. Zhang, J. Zhang, T.M. Lok, and M.R. Lyu A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training Appl. Math. Comput. 185 2007 1026 1037