Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
An Improved Approach Combining Random PSO with BP for Feedforward Neural Networks
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model)
Engineering Applications of Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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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.