Brief Introduction of HDPE Geomembrane Grey Prediction Model and Dynamic Prediction Model

HDPE geomembrane gray prediction is a prediction method based on grey system theory. HDPE geomembrane is based on the past and its now known or non-deterministic information to build a gray model GreyMode, referred to as GM, from the past to the future. By whitening the system model differential equations and solving them by the least-squares method, the undetermined coefficients in the prediction model are determined to determine the future development trend of the system. The advantage of this method is that there is no special requirement for the original data distribution characteristics and sample size.

With the help of non-equal spacing gray prediction model, HDPE geomembrane produced by HDPE geomembrane manufacturers can use creep deformation at high load level to predict creep deformation under low load level; on the other hand, it can be used to predict low creep rupt time under high load level. Creep rupture time at load level.

The dynamic prediction method is different from the static prediction method in that the original data processing system can be continuously adjusted according to the changing trend of the observed data, and the HDPE geomembrane can timely incorporate the measured data into the system, and the creep prediction value obtained thereby More reasonable.

With the help of non-equal spacing gray prediction model, HDPE geomembrane can use creep deformation at high load level to predict creep deformation under low load level; on the other hand, it can be used to predict low creep rupt time under high load level. Creep rupture time at load level.

The dynamic prediction method is different from the static prediction method in that the original data processing system can be continuously adjusted according to the changing trend of the observed data, and the high quality HDPE geomembrane can timely incorporate the measured data into the system, and the creep prediction value obtained thereby More reasonable.

A widely used B-network is widely used in the field of prediction. The learning algorithm of B network is a kind of error backpropagation network weight training method. Its theoretical basis is a multi-layer neural network model, which includes three layers: the input layer, hidden layer, and output layer. The input layer has n nodes, and the output layer has m nodes. A neural network is a complex nonlinear dynamic network system. A network with bias and at least one S-type hidden layer plus a line output layer can approximate any rational function. The unique non-linear ability of the small square and B neural network, HDPE geomembrane better solves the problem that the creep coefficient and the three-parameter method have low fitting accuracy to the creep data. But neural networks require a large amount of data as a learning and training network structure, HDPE geomembrane practice proved.