CN103258243A - Tube explosion predicting method based on grey neural network - Google Patents
Tube explosion predicting method based on grey neural network Download PDFInfo
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- CN103258243A CN103258243A CN2013101518441A CN201310151844A CN103258243A CN 103258243 A CN103258243 A CN 103258243A CN 2013101518441 A CN2013101518441 A CN 2013101518441A CN 201310151844 A CN201310151844 A CN 201310151844A CN 103258243 A CN103258243 A CN 103258243A
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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
The invention belongs to the urban water supply field, specifically is a kind of water supply network booster Forecasting Methodology based on grey neural network.
Background technology
Water supply network is one of the important foundation facility in city, also is the important component part of urban lifeline engineering.The pipe network booster can cause the waste of great lot of water resources, threatens water supply security, influences ordinary production and life.Historical leakage loss data are analyzed, set up effective booster forecast model, can from the source, control the pipe network leakage loss, accomplish prevention early, early find, safeguard scientifically and rationally, realize the ACTIVE CONTROL of leakage loss.
At present, the booster forecast model mainly comprises physical model and statistical model.Physical model is generally by the load of dissection on pipeline, the ability of the anti-load of pipeline, and degree, the scope of the inside and outside suffered corrosion of pipeline wait to predict pipeline accident.Statistical model is foundation with the historical booster data of pipe network then, sets up pipe explosion accident with the method for statistics and quantizes rule.In recent years, data-driven modeling technique based on artificial intelligence comes into one's own, and aspect booster forecasting research existing the application, in article Assessing pipe failure rate and mechanical reliability of water distribution networks using data driven modeling, propose and set up booster model based on artificial neural network and adaptability nerve-fuzzy inference system as Tabesh M. etc.
Yet, tradition booster model needs lot of data, as the pipe characteristic data, accurately and enough long pipeline operation maintenance historical datas etc., but water supply network system bulky complex, the detail record that leak source takes place is difficult to accurately, gathers comprehensively, is badly in need of the existing low volume data of research and utilization and carries out booster analysis and Forecasting Methodology.Simultaneously, the leakage loss of considering water supply network is subjected to the influence of various factorss such as the pipeline time limit, pipeline material, temperature, field engineering, uncertain factor is more, join together to regard as a big system if will influence the various complicated factors of pipe network booster, this system has determinacy concurrently with uncertain, can regard a typical gray system as.
The gray system modeling method can not considered the regularity of distribution, variation tendency, can find out the variation relation of system from a small amount of sample, and modeling method is simple.But gray system does not possess computation capability, and it is high that model accuracy is owed.And neural network can realize Nonlinear Mapping, has advantages such as the storage of parallel computation, distributed information, fault-tolerant ability are strong, adaptive learning function.If in conjunction with constituting Grey Neural Network Model, then advantage has concurrently, can solve the small sample forecasting problem preferably with both, improve precision of prediction.
Summary of the invention
The objective of the invention is to overcome the deficiency in the existing method, a kind of water supply network booster Forecasting Methodology based on grey neural network is proposed, precision of prediction can be improved effectively, and small sample prediction and large sample prediction can be applicable to simultaneously to pipe network booster historical record is less demanding.
This method is achieved through the following technical solutions: at first, for given booster factor and booster rate data sequence, by static grey modeling, booster rate sequence is predicted.Predicting the outcome compares with former booster rate sequence, obtains residual error.Then, utilize neural network between these residual sum booster factors, to set up the neural network approximate model.The neural network of process repetition training is exactly the mapping relations between the selected gray model data of residual sum.When predicting at last, again the predicted value of the gray model offset with neural network is compensated.
Concrete modeling process is as follows:
(1) collects, puts in order statistics booster data
The general factor that influences booster has: pipe workpiece quality, interface shape, caliber, buried depth of pipeline, temperature Change, physical features sedimentation and load, pipe network running pressure, corrosive pipeline etc.Wherein some factor can quantize, and some factor can't quantize.From the booster database, the quantifiable factor of statistical study such as caliber, buried depth, pipe network running pressure, pipe range etc., and calculate booster rate (the general year booster rate of calculating).
(2) set up model
Based on step (1) statistics collection
Individual booster factor and booster rate
, set up with
Individual booster factor is factor variable, is the behavior variable with the booster rate
Model.Concrete steps are as follows:
1) establishes
(this sequence is represented the behavior variable for the system features data sequence
Individual observed reading)
This
Individual sequence is called the (expression of correlative factor sequence
Individual factor variable individual observed reading separately).
The one-accumulate generation of above-mentioned each data sequence (
) sequence is designated as
(
), so-called one-accumulate generates namely:
If
(
) be original series,
Be the sequence operator
, wherein
, claim
For
The one-accumulate generating operator, and the new sequence that generates
Be the one-accumulate formation sequence.
Wherein:
3) applying step 2) in model predict that the behavior variable sequence that obtains predicting is
By above step, just set up with
Individual booster factor is factor variable, is the behavior variable with the booster rate
Model.
(3) set up neural network model
Correlative factor sequence with one-accumulate
(
) as the input of BP neural network (can use other neural network models), by
The characteristic sequence that model prediction obtains
Characteristic sequence with one-accumulate
Residual sequence
As the output of network, wherein
, set up the BP neural network model.
At first, be in the difference of the order of magnitude between state of saturation and data for fear of the hidden layer neuron, guarantee that network has enough input susceptibility and good fitness to sample, before the BP neural network is trained, carry out pre-service to learning auspicious notebook data.Namely all data are carried out normalized, sample data is converted into
Value on the interval.Certainly, when using through the network after the study, also should carry out anti-normalization to the output data of network, recover final predicted value.
Normalized specific algorithm is:
In the formula
---one group of collected data;
Anti-normalization specific algorithm is:
Then, the tool box of using among the Matlab comes training network with basic back-propagation algorithm (can adopt other learning algorithms), to obtain the corresponding weights of hidden layer and output layer.Like this, the neural network through repetition training is exactly the mapping relations of residual sequence and one-accumulate booster correlative factor sequence.
(4) prediction booster rate
During prediction, earlier will
The predicted value of model
Offset with neural network
Carry out error compensation, to obtain predicted value
Once tire out then to subtract to generate and obtain
What is called is once tired to subtract generation namely:
So far, through step (1), (2), and (3), (4) have just set up the water supply network booster forecast model of grey neural network.
The inventive method is set up Grey Neural Network Model in conjunction with grey modeling method and neural network model, overcoming traditional booster model needs the shortcoming of lot of data, can solve the small sample forecasting problem preferably, improve precision of prediction, simultaneously equally suitable for large sample.Especially for the record and the late water undertaking of maintenance starting of booster data, this method presses for.
Description of drawings
Fig. 1 is theory diagram of the present invention.
Embodiment
Provide an embodiment below, the specific embodiment of the present invention is described in further detail.Following examples only are used for explanation the present invention, but are not used for limiting the scope of the invention.
(1) collects, puts in order statistics booster data
From the booster database in certain zone of supplying water, the caliber of statistics pipeline, pipe age, pressure data, and calculate booster rate (the general year booster rate of calculating).
Concrete statistical method is:
Then, calculate total pipe range of every group
:
Based on the weighted mean pipe age of pipe range
:
And the average booster number of times of annual per unit pipe range and booster rate
:
Wherein
Be the booster rate, unit is
Be statistics year numbering;
Be
Group statistics year booster number of times;
Be statistics year sum.
Based on 3 booster factors and the booster rate of step (1) statistics collection, altogether
Group.Foundation is with caliber
, the pipe age
, pressure
Be factor variable, with the booster rate
For the behavior variable
Model.Concrete steps are as follows:
Be pressure
Sequence;
The one-accumulate generation of above-mentioned each data sequence (
) sequence is designated as
(
), so-called one-accumulate generates namely:
If
(
) be original series,
Be the sequence operator
, wherein
, claim
For
The one-accumulate generating operator, and the new sequence that generates
Be the one-accumulate formation sequence.
3) applying step 2) in model predict behavior variable sequence (the booster rate that obtains predicting
) be
(3) set up neural network model
Correlative factor sequence with one-accumulate
(
) as the input of BP neural network (can use other neural network models, present embodiment adopts the BP neural network), by
Model prediction obtains sequence
With sequence
Residual sequence
As the output of network, wherein
Then, the tool box of using among the Matlab comes training network with basic back-propagation algorithm (can other corresponding learning algorithms), to obtain the corresponding weights of hidden layer and output layer.Like this, the neural network through repetition training is exactly the mapping relations of residual sequence and one-accumulate booster correlative factor sequence.
Concrete steps are as follows:
1) with the correlative factor sequence of one-accumulate
(
) constitute the input matrix of BP neural network
With residual sequence
Sharp formation network output matrix
2) data normalization is handled.Utilize the normalization formula
Respectively the input and output matrix is carried out normalized.
3) make up the BP neural network
Present embodiment uses three layers of feedforward neural network.Form input layer by three neurons, i.e. caliber behind one-accumulate
, the pipe age
, pressure
A hidden layer, the neuron number of hidden layer be border problem complexity and deciding factually, and this example adopts 12 neurons (as the tubing factor, pipe joint problem, temperature variation, factors such as buried depth of pipeline); Output layer is made up of a neuron, i.e. the booster rate
Concrete steps are as follows:
A. build the BP neural network framework, call the newff function in the Matlab function library
Net=newff(minmax(P’),[12,1],'tansig','purelin','traingdm')
Wherein minmax (P ') is minimum and the maximal value of the every row of matrix P ', and [12,1] expression hidden layer has 12 neurons, and output layer has 1 neuron; Tansig is the input layer transport function; Purelin is the output layer transport function; Trainlm is the training function based on the l-m algorithm.
B. train the BP neural network
A. initialization network.Net.initFcn decides the initialization function of whole network.The parameter net.layer{i}.initFcn initialization function that decides each layer.The initwb function according to the initiation parameter of each layer oneself (initializes weights is made as rands usually for net.inputWeights{i, j}.initFcn) initializes weights matrix and biasing, and concrete grammar is as follows:
net.layers{1}.initFcn=’initwb’;
net.inputWeights{1,1}.initFcn=’rands’;
net,layerWeights{2,1}.initFcn=’rands’;
net.biases{1,1}.initFcn=’rands’;
net.biases{1,1}.initFcn=’rands’;
net=init(net);
Net.IW{1,1} be input layer to the weight matrix of hidden layer,
Net.LW{2,1} are that hidden layer is to the weight matrix of output layer;
Net.b{1,1} are the threshold values vector of hidden layer,
Net.b{2,1} are the threshold values of output contact;
B., the step number that network training number of times, training objective error is set and is used for showing
net.trainParam.epochs=1500;
net.trainParam.goal=0.0008;
net.trainParam.show=100;
It was 1500 steps that the network training number of times is set, and the training objective error is 0.0008, showed that the training step number is 100.
C. utilize input matrix
And output matrix
, by calling the train function, net=train (net, P ', T ') carries out network training until convergence.
The mapping relations of residual sequence and one-accumulate booster correlative factor sequence have so just been set up.
(4) prediction booster rate
Prediction steps is as follows:
1) with the prediction output matrix of neural network
, use formula
Carry out anti-normalization, obtain the prediction residual matrix
2) will
The predicted value of model
The residual matrix that obtains with neural network prediction
Carry out error compensation, to obtain predicted value
Once tire out then to subtract to generate and obtain
What is called is once tired to subtract generation namely:
Claims (1)
1. based on the booster Forecasting Methodology of grey neural network, it is characterized in that this method may further comprise the steps:
Step (1) is collected booster factor, arrangement statistics booster data and is calculated the booster rate, and described booster data are quantifiable booster factor, comprise caliber, buried depth, pipe network running pressure and pipe range;
Step (2) is set up
Model;
Based on step (1) statistics collection
Individual booster factor and 1 booster rate
, set up with
Individual booster factor is factor variable, is the behavior variable with the booster rate
Model, concrete steps are as follows:
1) establishes
Be the system features data sequence, this sequence is represented the behavior variable
Individual observed reading;
This
Individual sequence is called the correlative factor sequence, expression
Individual factor variable separately
Individual observed reading;
The one-accumulate of above-mentioned each data sequence generates
Sequence is designated as
,
, so-called one-accumulate generates namely:
Claim
For
The one-accumulate generating operator, and the new sequence that generates
Be the one-accumulate formation sequence;
Argument List wherein
, available least-squares estimation and obtain into
Wherein:
3) applying step 2) in model predict that the behavior variable sequence that obtains predicting is:
By above step, just set up with
Individual booster factor is factor variable, is the behavior variable with the booster rate
Model;
Step (3) is set up neural network model;
Correlative factor sequence with one-accumulate
As the input of BP neural network, by
The characteristic sequence that model prediction obtains
Characteristic sequence with one-accumulate
Residual sequence
As the output of network, wherein
, set up the BP neural network model;
At first, be in the difference of the order of magnitude between state of saturation and data for fear of the hidden layer neuron, guarantee that network has enough input susceptibility and good fitness to sample, before the BP neural network is trained, carry out pre-service to learning auspicious notebook data; Namely all data are carried out normalized, sample data is converted into
Value on the interval; When using through the network after the study, also should carry out anti-normalization to the output data of network, recover final predicted value;
Normalized specific algorithm is:
In the formula
Represent one group of collected data;
Represent the minimum value in these group data;
Represent the maximal value in these group data;
Data after the expression mapping;
Anti-normalization specific algorithm is:
Then, the tool box of using among the Matlab comes training network with basic back-propagation algorithm, to obtain the corresponding weights of hidden layer and output layer; Like this, the neural network through repetition training is exactly the mapping relations of residual sequence and one-accumulate booster correlative factor sequence;
Step (4) prediction booster rate;
During prediction, earlier will
The predicted value of model
Offset with neural network
Carry out error compensation, to obtain predicted value
Once tire out then to subtract to generate and obtain
What is called is once tired to subtract generation namely:
So far, through step (1), (2), and (3), (4) have just set up the water supply network booster forecast model of grey neural network.
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CN109886506A (en) * | 2019-03-14 | 2019-06-14 | 重庆大学 | A kind of water supply network booster risk analysis method |
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Application publication date: 20130821 Assignee: HANGZHOU ZHONGZI FENGTAI ENVIRONMENT TECHNOLOGY Co.,Ltd. Assignor: HANGZHOU DIANZI University Contract record no.: X2020330000109 Denomination of invention: Prediction method of tube burst based on Grey Neural Network Granted publication date: 20161130 License type: Common License Record date: 20201129 |