The Application and Study of A Neural Network Model Based on Multivariate Phase Space Reconstruction
For multi-variable nonlinear system evolution with time-varying, a neural network model based on multi-variable phase-space reconstruction has been proposed, and is used in civil engineering for synthesized deformation prediction of deep foundation pit. By the various time series time delay and embedding dimension determined respectively in this model. the multi-variable series of excavation deformation for deep foundation pit has been done in the first phase space reconstructiom The neural network input extraction by the use of partial least squares regression method can be the strongest impact components. Finally non-linear fitting between the various components has been completed via BP neural network modeL With practical application for deformation prediction of deep foundation pit, the methods effectiveness has been verified.
artijicial neural network Phase space reconstruction multivariable nonlinear system foundation pit deformation prediction
XI Xue-feng FU Bao-chuan LU Wei-zhong LI An-yong
School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou, School of Civil Engineering Suzhou University of Science & Technology Suzhou, China
国际会议
成都
英文
296-300
2010-06-23（万方平台首次上网日期，不代表论文的发表时间）