In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load-deformation behaviour of axially loaded piles. This is a soil-structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile loading tests is used to train and validate the product-unit network. The results show that the proposed hybrid learning algorithm simulates the load-deformation curve of axially loaded piles more accurately than other BP, PSO, and existing PSO-BP hybrid methods. The network developed using the proposed algorithm also turns out to be more accurate than hyperbolic and t-z models.
|Number of pages||10|
|Journal||Engineering Applications of Artificial Intellegence|
|Publication status||Published - Nov 2013|
Ismail, A., Jeng, D-S., & Zhang, L. L. (2013). 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 Intellegence, 26(10), 2305-2314. https://doi.org/10.1016/j.engappai.2013.04.007