Study on MPGA-BP of Gravity Dam Deformation Prediction

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy

An enhanced training algorithm for multilayer neural networks based on reference output of hidden layer

In this paper, the authors propose a new training algorithm which does not only rely upon the training samples, but also depends upon the output of the hidden layer. We adjust both the connecting weights and outputs of the hidden layer based on Least Square Backpropagation (LSB) algorithm. A set of ‘required’ outputs of the hidden layer is added to the input sets through a feedback path to accelerate the convergence speed. The numerical simulation results have demonstrated that the algorithm is better than conventional BP, Quasi-Newton BFGS (an alternative to the conjugate gradient methods for fast optimisation) and LSB algorithms in terms of convergence speed and training error. The proposed method does not suffer from the drawback of the LSB algorithm, for which the training error cannot be further reduced after three iterations