Abstract：The model of wavelet neural network(WNN) is optimized by using adaptive particle swarm optimization (APSO)，which is named as wavelet neural network based on adaptive particle swarm optimization(APSO-WNN). The optimized model combines good time domain，frequency domain，and good resolving power of the wavelet transform，self-study of traditional neural network，and quick convergence to the optimum solution of the adaptive particle swarm optimization. Therefore，it has the advantages of great efficiency and good fault-tolerance and robustness，which makes it easy to solve the geotechnical engineering problem with characteristic of fuzziness and nonlinearity. For comparison，the models of APSO-WNN and GA-ANN(artificial neural network optimized by genetic algorithm) are used to calculate the elastic modulus of the high slope of the Three Gorges Project on the basis of the measured displacements. The result shows that the former model takes smaller time compared with GA-ANN in a same precision level. Those show that APSO-WNN model is an excellent model. At last，the calculated elastic modulus is used to forecast the displacements of the monitoring points of high slope of the Three Gorges Project. Forecasting values are in good agreement with the measured values，which indicate that the APSO-WNN model can be well applied to the displacement back analysis in geotechnical engineering.
