Deformation prediction method and apparatus based on Kalman filtering and BP neural network

Abstract

The invention provides a deformation prediction method and apparatus based on Kalman filtering and a BP neural network, wherein the deformation prediction method based on the Kalman filtering and the BP neural network comprises: obtaining the deformation monitoring data of a monitored object in a project as a training sample; establishing a BP neural network topology model according to the training sample; learning the BP neural network topology model by using a Kalman filtering algorithm according to preset training parameters to adjust the weights of the neurons in the BP neural network topology model; and performing deformation prediction according to the BP neural network topology model with adjusted weights. The deformation prediction method based on Kalman filtering and a BP neural network can shorten the learning time of the BP neural network and improve the establishment efficiency of deformation prediction model in a deformation prediction process.

Description

technical field [0001] The present invention relates to the field of deformation prediction of dynamic systems, in particular to a deformation prediction method and device based on Kalman filter and back propagation (Back Propagation, BP for short) neural network. Background technique [0002] During the construction and operation of modern engineering buildings, due to the deformation of the earth's crust and surface caused by human activities (such as pumping groundwater, oil extraction, mining, etc.), or the foundation design of engineering buildings is unreasonable, the ground treatment is unscientific, or natural Under the influence of disasters and other factors, it is easy to produce deformation. If the deformation exceeds the specified limit, it will affect the normal use of engineering buildings. Therefore, deformation monitoring and deformation prediction have a very important impact on engineering buildings. Usually, based on the deformation monitoring data, a de...

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Problems solved by technology

[0004] However, since the learning rate of the BP neural network is fixed, the convergence speed of the BP neural network algorithm is relatively slow and requires a long learning time. For some complex problems,

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