The Application Of Wavelet Neural Network In Settlement Predicition

Settlement observation involved all through the design period,construction period, operation period of engineering structure. All engineering and technical staffs have paid much attention and strict analysis to the important problem which should not be neglected. Settlement forecast methods could be divided into theoretical methods and experimental method that based on calculating field data. There are many influence factors of settlement which are uncertain. Because of all the factors have influenced settlement in subsidence observation, the second methods have been widely used. This paper discussed the defects of traditional settlement prediction models, researched wavelet neural network of settlement prediction model, then put forward measures and approaches for improved wavelet neural network based on BP algorithm and applied in engineering examples. The main contents of this paper are as follows:1. The wavelet neural network arithmetic is studied. Uniting the study of artificial neural networks and its combination with wavelet transform and BP networks' fruit before, the defects of wavelet neural network based on BP algorithm were analyzed, and a new unconstrained line search is introduced to improve conjugate gradient methods, then derived DY-HS crossing conjugate gradient back propagation algorithm under the new line search to train wavelet neural network, which solve the problem of easily relapsing into local minimization of classical wavelet neural network based on BP algorithm.2. The self-correlation correction for initial weights of wavelet neural network is discussed. This method integrates the setting of initial parameters with the wavelet type, time-frequency parameters of the wavelet and the training samples, and it is different from traditional method which get initial weights randomly and the stability and convergence precision of network is improved.3. The configuration of wavelet neural network is optimized. The improved waveletneural network based on DY-HS crossing conjugate gradient back peopagation algorithm is put forward with colligating improved method above which is studied. And a simple form of varied structure is introduced to regulate the node of hidden layer of network. The improved wavelet neural network optimize the classical wavelet network based on BP algorithm, can effectively conquer the shortages of classical wavelet network based on BP algorithm.4. The BP network,BP wavelet network and improved wavelet network is used insetting up the settlement forecasting model. The selection of training samples were adopted into two schemes: the total settlement and the interval settlement, then applied the two schemes in forecasting different engineering examples. Compared with various forecasting results, the improved wavelet network model was better than other model, selecting training different samples according to different sinking periods would acquired higher prediction precision.