The Combined Forecasting Model of Discrete Verhulst-BP Neural Network Based on Linear Time-Varying

Shang Hongchao, Long Xia, He Tingjie


Firstly, this paper, aiming at the problem of errors produced by the transformation of differential equation directly into difference equation from traditional gray Verhulst model,through generating reciprocal for the original data sequence, constructs the discrete Vrhulst model based on linear time-varying(LTDVM model);And then we, taking the LTDVM predicted value as an input value and the original data as a mentor training value, put forward the combined forecasting model of discrete Verhulst-BP neural network based on linear time-varying. Meanwhile, in order to improve the training speed and agility and effectively avoid the saturation region of S-type function, this article normalized in advance the input data and mentor training values to better ensure the usefulness, self-learning ability and fault tolerance of the model. At last, we will study the cases to demonstrate that the model has high modeling and forecasting accuracy.



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