Tube explosion predicting method based on grey neural network

Abstract

The invention discloses a tube explosion predicting method based on a grey neural network. The tube explosion predicting method based on the grey neural network comprises the steps that firstly predicting is conducted on tube explosion rate sequences through static grey modeling according to given tube explosion factors and tube explosion rate data sequences, and predicting results and the original tube explosion rate sequences are compared to obtain residual errors; then neural network approximate models are established among the residual errors and the tube explosion factors by using the neural network, and the neural network being repeatedly trained is a mapping relation among the residual errors and grey model data; in the final predicting process, predicted values of the grey models are compensated by compensation values of the neural network. According to the tube explosion predicting method based on the grey neural network, the grey neural network models are established by combining a grey modeling method and a neural network model, the defect that a traditional tube explosion model needs a large amount of data is overcome, problems of predicting small samples can be excellently solved, and predicting accuracy is improved.

Description

technical field [0001] The invention belongs to the field of urban water supply, in particular to a gray neural network-based pipe burst prediction method for a water supply pipe network. Background technique [0002] The water supply network is one of the important infrastructures of the city and an important part of the urban lifeline project. The bursting of the pipe network will cause a lot of waste of water resources, threaten the safety of water supply, and affect normal production and life. Analyzing historical leakage data and establishing an effective pipe burst prediction model can control pipeline network leakage from the source, achieve early prevention, early detection, scientific and reasonable maintenance, and realize active control of leakage. [0003] At present, pipe burst prediction models mainly include physical models and statistical models. The physical model generally predicts pipeline accidents by analyzing the load acting on the pipeline, the abili...

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Problems solved by technology

However, the gray system does not have parallel computing capability, and the model accuracy is not high.

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