Non-equidistant gray optimization model and its application to the foundation settlement prediction
MA Fu-xu · Journal of Heilongjiang Institute of Technology · 2014
Traditional non-equidistant gray model usually uses non-equal interval data fo piecewise linear interpolation in order to obtain equally-spaced sequence.Because the actual foundation settlement is not linear variation,the equally-spaced squence exists with greater errors than the actual data.In view of the traditional non-equidistant gray model defects,the RBF neural network interpolation and cubic spline interpolation are used to enerate equidistant sequence to obtain the model parameters.An optimized nonequidistant gray model is established for analysis prediction of a foundation settlement.The calculation results show that the optimized model has higher accuracy which can be used as a new method for the foundation settlement prediction.