Deformation monitoring is a requisite for dam safety monitoring. Due to a large number of factors, neural networks such as back propagation (BP) and radial basis function (RBF) are often used for parameters selection and model establishment, of which RBF has been widely employed on account of its simple network structure and rapid convergence. Nonetheless, local optimality and inappropriate selection of parameters will exert great impact on the convergence rate. In view of this, the Particle Swarm Optimization with Inertia Weight (referred to as WPSO) is adopted to optimize three parameters of RBF (central value c of hidden layer base function parameter, width d and connection weight w between hidden layer and output layer parameter). In subsequence, the WPSO-RBF model is integrated as a weaker classifier by AdaBoost algorithm, hence establishing a WPSO-RBF-AdaBoost model for dam deformation monitoring. The model is applied to practical engineering, and results suggest that the present model is of fast convergence, high classification precision and good generalization ability.
Key words
dam deformation /
monitoring model /
Particle Swarm Optimization with Inertia Weight /
RBF neural network /
AdaBoost algorithm
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