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CN102737288A - Radial basis function (RBF) neural network parameter self-optimizing-based multi-step prediction method for water quality - Google Patents

Radial basis function (RBF) neural network parameter self-optimizing-based multi-step prediction method for water quality Download PDF

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Publication number
CN102737288A
CN102737288A CN2012102048383A CN201210204838A CN102737288A CN 102737288 A CN102737288 A CN 102737288A CN 2012102048383 A CN2012102048383 A CN 2012102048383A CN 201210204838 A CN201210204838 A CN 201210204838A CN 102737288 A CN102737288 A CN 102737288A
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water quality
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rbf neural
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侯迪波
陈玥
黄平捷
张光新
何慧梅
刘洋
包莹
赵海峰
郭诚
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Zhejiang University ZJU
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Abstract

The invention discloses a radial basis function (RBF) neural network parameter self-optimizing-based multi-step prediction method for water quality. The method comprises the following steps of: first storing the data of each monitoring station into a database of a local server by using the remote transmission of an online water quality monitoring instrument; then performing normalization processing on a water quality sample sequence, calculating an autocorrelation coefficient to determine an input variable of an RBF neural network, and converting sample data into a standard dynamic sequence data format trained and predicted by the RBF neutral network; next searching for and determining an optimal value of a spreading coefficient spread of the RBF neural network by utilizing a differential evolution algorithm and taking a relative standard error as a target function to obtain an optimal prediction model; and finally sampling water quality data in real time, performing multi-step prediction by using the obtained optimal prediction model and adopting a single-point iteration method, and evaluating a water quality prediction result to realize an early warning function. The water quality can be intelligently warned.

Description

A kind of water quality multistep forecasting method based on the self-optimizing of RBF neural network parameter
Technical field
The present invention relates to a kind of water quality prediction method, relate in particular to a kind of water quality multistep forecasting method based on the self-optimizing of RBF neural network parameter.
Background technology
Drinking water safety carries out the real-time estimate analysis concerning national economy to water quality, can effectively control and reduces the harm that water quality deterioration causes, and reaches the effective cognition to water quality deterioration, the target of control.In addition, the water quality multi-step prediction can win the more emergency response time for water factory timely and effectively.
Main both at home and abroad at present research single step water quality prediction method; The single step water quality prediction comprises modeling based on mechanism, based on two aspects of modeling of intelligence; Because Water Environment System is complicated and changeable; Its details mechanism can not be understood fully, and the latter has the good information processing power to the indefinite high dimensional nonlinear of mechanism system, in water quality prediction, has obtained in recent years using widely.Neural network has powerful self-organization, self study, parallel processing information and non-linear fault-tolerant ability; It is the focus of studying both at home and abroad; But only know the predicted value of next time point; Can not be for the water quality early-warning system provides the sufficiently long emergency processing time, so be necessary to introduce the method for multi-step prediction.
Summary of the invention
The objective of the invention is to propose a kind of water quality multistep forecasting method, not only satisfied the requirement of multi-step prediction precision, also realized intelligentized water quality early-warning well based on the self-optimizing of RBF neural network parameter.
The objective of the invention is to realize through following technical scheme: a kind of water quality multistep forecasting method based on the self-optimizing of RBF neural network parameter comprises the steps:
(1) through the remote transmission of online water quality monitoring instrument, the data of each monitoring station are deposited in the database of home server, form the water quality sample sequence, be convenient to water quality parameter is analyzed; The said good and bad water quality parameter of evaluating water quality that is used for comprises: pH, conductivity, turbidity, dissolved oxygen DO, temperature, ammonia nitrogen, chlorine residue and permanganate indices etc.;
(2) the water quality sample sequence being carried out normalization handles; Confirm the input variable of RBF neural network through calculating coefficient of autocorrelation; Sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction: the water quality sample sequence is for
Figure 2012102048383100002DEST_PATH_IMAGE002
; According to
Figure 2012102048383100002DEST_PATH_IMAGE004
data being carried out normalization handles; Wherein,
Figure 2012102048383100002DEST_PATH_IMAGE006
and
Figure 2012102048383100002DEST_PATH_IMAGE008
is respectively maximal value and minimum value in
Figure 667668DEST_PATH_IMAGE002
; The calculating of coefficient of autocorrelation is shown below:
Figure 2012102048383100002DEST_PATH_IMAGE010
?,
In the formula;
Figure 2012102048383100002DEST_PATH_IMAGE012
is autocorrelation function;
Figure 2012102048383100002DEST_PATH_IMAGE014
is coefficient of autocorrelation to be asked;
Figure 2012102048383100002DEST_PATH_IMAGE016
is the length of water quality time series
Figure 83737DEST_PATH_IMAGE002
; N is a water quality sample ordinal number, and
Figure 2012102048383100002DEST_PATH_IMAGE018
is the water quality sample.
The scope of coefficient of autocorrelation is
Figure 2012102048383100002DEST_PATH_IMAGE020
; Set correlation coefficient threshold
Figure 2012102048383100002DEST_PATH_IMAGE022
; During as
Figure 2012102048383100002DEST_PATH_IMAGE024
; is so the input vector of RBF neural network
Figure 2012102048383100002DEST_PATH_IMAGE028
is configured to
Figure 2012102048383100002DEST_PATH_IMAGE030
.Confirm the length
Figure 2012102048383100002DEST_PATH_IMAGE032
of training sample according to forecasting problem actual demand and sample data characteristics; Obtain the inputoutput pair
Figure 2012102048383100002DEST_PATH_IMAGE034
of network, sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction;
(3) with error criterion poor as objective function; Utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread; Obtain optimum forecast model: utilize the newrbe function to set up the RBF neural network, utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread; The differential evolution algorithm is a kind of global optimization method based on the population evolution of floating-point code; The minimum problem of promptly finding the solution
Figure 2012102048383100002DEST_PATH_IMAGE038
; Wherein
Figure 914159DEST_PATH_IMAGE018
is spread, objective function
Figure 2012102048383100002DEST_PATH_IMAGE040
be error criterion poor expression as shown in the formula:
Figure 2012102048383100002DEST_PATH_IMAGE042
Wherein
Figure 355078DEST_PATH_IMAGE018
is original water quality sequence;
Figure 2012102048383100002DEST_PATH_IMAGE044
is the water quality sequence of prediction, and is training sample length;
(4) real-time sampling water quality data with the optimum prediction model that step 3 obtains, adopt the method for single-point iteration to realize multi-step prediction, and evaluating water quality predicts the outcome the effect of realization early warning; If exceed normal range then warning immediately.
The invention has the beneficial effects as follows; The present invention proposes to confirm through coefficient of autocorrelation the single-point process of iteration of RBF neural network input variable; Improve the effect of multi-step prediction, utilized the optimal value of differential evolution algorithm search spreading coefficient spread simultaneously, realized efficient intelligent robotization early warning.
Description of drawings
Fig. 1 utilizes coefficient of autocorrelation to confirm the method figure of RBF neural network input;
Fig. 2 is the sequence coefficient of autocorrelation figure of water quality parameter (is example with pH);
Fig. 3 utilizes the differential evolution algorithm to carry out the schematic diagram that the spread parameter is selected;
Fig. 4 is the differential evolution algorithm search figure as a result of spread;
Fig. 5 is the figure that predicts the outcome in 4 steps of water quality parameter (is example with pH);
Fig. 6 is 4 step predicated error curve maps of water quality parameter (is example with pH).
Embodiment
The step of water quality multistep forecasting method that the present invention is based on the self-optimizing of RBF neural network parameter is following:
1, through the remote transmission of online water quality monitoring instrument, the data of each monitoring station are deposited in the database of home server, form the water quality sample sequence, be convenient to water quality parameter is analyzed.
Being used for the good and bad water quality parameter of evaluating water quality mainly contains conventional Wucan number (pH, conductivity, turbidity, dissolved oxygen DO, temperature), ammonia nitrogen, chlorine residue, permanganate indices etc.; The different quality parameter sensors of various places monitoring station was whenever surveyed once at a distance from 15 minutes; Every survey one secondary data just is deposited into data in the database server; Set up the table in the database according to different websites and different quality parameter; This database is used for preserving all raw data from each monitoring station monitoring, is convenient to water quality parameter is carried out analyzing and processing.
2, the water quality sample sequence is carried out normalization and handle, confirm the input variable of RBF neural network, sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction through calculating coefficient of autocorrelation.
If the water quality sample sequence is
Figure 869553DEST_PATH_IMAGE002
; The neural network of building in order to guarantee has enough input sensitivitys and good fitness for sample; According to
Figure 375621DEST_PATH_IMAGE004
data are carried out normalization and handle, wherein maximal value and the minimum value in
Figure 529522DEST_PATH_IMAGE006
and
Figure 412027DEST_PATH_IMAGE008
difference .Because coefficient of autocorrelation can be weighed the degree of correlation between the data on the time series different time points,, confirm the input vector of RBF neural network so adopt the autocorrelation analysis method to analyze and the most closely-related time series historical data of predicted value.Because the length of sequence is limited in the reality, so the calculating of coefficient of autocorrelation is suc as formula shown in (1):
Figure 143277DEST_PATH_IMAGE010
(1)
In the formula (1);
Figure 518895DEST_PATH_IMAGE012
is autocorrelation function;
Figure 939512DEST_PATH_IMAGE014
is coefficient of autocorrelation to be asked; is the length of water quality time series
Figure 197635DEST_PATH_IMAGE002
; N is a water quality sample ordinal number, and is the water quality sample.
The scope of coefficient of autocorrelation is
Figure 284857DEST_PATH_IMAGE020
; Set correlation coefficient threshold
Figure 611933DEST_PATH_IMAGE022
; During as ;
Figure 747958DEST_PATH_IMAGE026
is so the input vector of RBF neural network
Figure 448061DEST_PATH_IMAGE028
is configured to
Figure 567327DEST_PATH_IMAGE030
.Confirm the length
Figure 22579DEST_PATH_IMAGE032
of training sample according to forecasting problem actual demand and sample data characteristics; Obtain the inputoutput pair of network, sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction.
3, with error criterion poor
Figure 862414DEST_PATH_IMAGE036
as objective function; Utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread, obtain optimum forecast model.
Utilize the newrbe function to set up the RBF neural network; This function has automatic adjustment hidden layer unit number, and to make error be 0 function, but in RBF network operations process, need manually to change the degree of fitting that its spreading coefficient spread value is adjusted whole network; Manual adjustment often relatively blindly; Inefficiency not only, and differ and find optimum solution surely, so utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread.The differential evolution algorithm is a kind of global optimization method based on the population evolution of floating-point code; The minimum problem of promptly finding the solution
Figure 898503DEST_PATH_IMAGE038
; Wherein
Figure 462340DEST_PATH_IMAGE018
is spread, and objective function
Figure 849459DEST_PATH_IMAGE040
is that error criterion poor
Figure 953681DEST_PATH_IMAGE036
is represented suc as formula (2):
(2)
Wherein
Figure 844593DEST_PATH_IMAGE018
is original water quality sequence;
Figure 391112DEST_PATH_IMAGE044
is the water quality sequence of prediction, and
Figure 564605DEST_PATH_IMAGE032
is training sample length.
Suppose that each has individuals in generation
Figure 2012102048383100002DEST_PATH_IMAGE046
in the differential evolution algorithm; representative is shown , and wherein
Figure 2012102048383100002DEST_PATH_IMAGE052
is
Figure 2012102048383100002DEST_PATH_IMAGE054
individuals in
Figure 795439DEST_PATH_IMAGE046
generation.
The concrete steps of selecting based on the spread parameter of differential evolution algorithm are following:
1) in parameter space, produces initial value at random for
Figure 891571DEST_PATH_IMAGE048
individuals; With each group parameter training RBF neural network, obtain corresponding error criterion poor
Figure 773814DEST_PATH_IMAGE036
then;
2) establish ;
3) utilize formula
Figure 2012102048383100002DEST_PATH_IMAGE058
to make a variation for current individuality
Figure 842265DEST_PATH_IMAGE052
; In the middle of producing individual , in the formula
Figure 2012102048383100002DEST_PATH_IMAGE062
;
4) middle individual
Figure 918805DEST_PATH_IMAGE060
and the current individuality that variation are obtained are hybridized; Obtain the candidate individual
Figure 2012102048383100002DEST_PATH_IMAGE064
of current individuality; If satisfied goes up equally distributed random number and is not more than the hybridization factor; Then
Figure 2012102048383100002DEST_PATH_IMAGE068
, otherwise
Figure 2012102048383100002DEST_PATH_IMAGE070
;
5) it is poor to utilize new argument
Figure 78182DEST_PATH_IMAGE064
training RBF neural network to obtain its error criterion; if
Figure 2012102048383100002DEST_PATH_IMAGE072
; Then
Figure 2012102048383100002DEST_PATH_IMAGE074
, otherwise ;
6)
Figure 2012102048383100002DEST_PATH_IMAGE078
; if
Figure 2012102048383100002DEST_PATH_IMAGE080
; Then be back to step 3), otherwise jump to step 7);
7) it is poor to find out minimum error criterion, and to note corresponding individuality be the spread value;
8) if the evolution number of times greater than 100, then export optimum spread value, otherwise be back to step 3).
4, real-time sampling water quality data with the optimum prediction model that step (3) obtains, adopt the method for single-point iteration to realize multi-step prediction, and evaluating water quality predicts the outcome the effect of realization early warning.
Sensor in real time sampling water quality data.In water quality early-warning, for advanced processing alert and definite emergency preplan, need obtain the predicted value of following water quality parameter in advance, adopt the method for single-point iteration to realize multi-step prediction, can shift to an earlier date the generation of foreseeing alert for more time.The method of single-point iteration specifically is embodied as; Water quality parameter predicted value
Figure 2012102048383100002DEST_PATH_IMAGE086
constantly that the optimum RBF neural network that adopts the front to build obtains
Figure 2012102048383100002DEST_PATH_IMAGE084
through
Figure 2012102048383100002DEST_PATH_IMAGE082
; Replace actual value to obtain the predicted value of any down with predicted value as the element of prediction input vector; Adopt this alternative manner can obtain the predicted value in following a plurality of moment; Along with the increase of predicting step number can make precision of prediction reduce; But be implied with the ductwork water quality parameter of some cycles property through the analysis of coefficient of autocorrelation method; Can be the numerical value of the special time of being separated by (Cycle Length) also as input variable; Avoid sample to replace the precision of prediction that actual value caused to descend, improve the effect of multi-step prediction by too much predicted value.With reference to GB5749-2006 " drinking water sanitary standard " regulation, the water quality prediction parameter is estimated in real time, if exceed normal range then report to the police immediately,, avoid the deterioration of alert so that relevant departments in time take appropriate measures.
Embodiment:
As shown in Figure 1; Get the somewhere pipe network water water quality parameter of on November 10th, 2011 to on-line monitoring on the 19th in November (is example with pH), real-time watch device monitoring in 15 minutes once has 960 data; Get 768 preceding 8 days data as the prediction training set, 192 back 2 days data are as test set.If time series is
Figure 312723DEST_PATH_IMAGE002
; The neural network of building in order to guarantee for sample have enough input sensitivitys and with good fitness; According to
Figure 622481DEST_PATH_IMAGE004
training set is carried out normalization and handle, wherein
Figure 630889DEST_PATH_IMAGE006
and
Figure 684295DEST_PATH_IMAGE008
is respectively the maximal value and the minimum value of training set.Calculate the coefficient of autocorrelation
Figure 461759DEST_PATH_IMAGE014
of pH value training set sequence then; The threshold value
Figure 942418DEST_PATH_IMAGE022
of setting related coefficient is 0.9; As shown in Figure 2; Coefficient of autocorrelation
Figure 674007DEST_PATH_IMAGE026
during as
Figure 2012102048383100002DEST_PATH_IMAGE088
; Therefore the input variable of RBF neural network can be configured to ; The inputoutput pair of training set network is
Figure 2012102048383100002DEST_PATH_IMAGE092
, so just sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction.
As shown in Figure 3;
Figure 468788DEST_PATH_IMAGE048
individuals of differential evolution algorithm makes a variation in parameter space, hybridizes, evolves; Every evolution generation is just with the new spread value training RBF neural network that produces; And it is poor to calculate the pairing error criterion of each parameter, judges whether to satisfy stop condition then, if the evolution number of times is greater than 100; Then export optimum spread value, otherwise the new parameter of generation that continues to evolve.As shown in Figure 4; Limited the locally optimal solution of differential evolution algorithm in can the quick lock in region of search, and finally confirm globally optimal solution just searched optimum solution after 4 times evolving; The spread optimal value that searches is 192, and minimum error criterion difference is 0.0079.
Water quality parameter predicted value
Figure 2012102048383100002DEST_PATH_IMAGE096
constantly that the optimum RBF neural network that adopts the front to build obtains through
Figure 2012102048383100002DEST_PATH_IMAGE094
; Replace element acquisition
Figure 2012102048383100002DEST_PATH_IMAGE098
constantly the predicted value of actual value with predicted value as the prediction input vector; So iterate can obtain
Figure DEST_PATH_IMAGE100
constantly predicted value, wherein
Figure 63903DEST_PATH_IMAGE016
is prediction step.Here choose
Figure DEST_PATH_IMAGE102
; Promptly predict the pH value after 1 hour; Final multi-step prediction result and graph of errors are respectively like Fig. 5 and shown in Figure 6; Can be known that by test result the water quality multistep forecasting method based on the self-optimizing of RBF neural network parameter of the present invention can be realized prediction well, the predicated error of 82% data is less than 0.03 in the prediction of 4 steps; Can obtain the following predicted value of water quality parameter constantly in advance through the method; With reference to GB5749-2006 " drinking water sanitary standard " regulation, the limit value of pH is to be not less than 6.5 and be not more than 8.5, and the water quality prediction parameter is estimated in real time; If report to the police not in normal range then immediately; So that relevant departments in time take appropriate measures, avoid the deterioration of alert to reach definite emergency preplan by the advanced processing alert, realize the effect of water quality early-warning.

Claims (1)

1. the water quality multistep forecasting method based on the self-optimizing of RBF neural network parameter is characterized in that, comprises the steps:
(1) through the remote transmission of online water quality monitoring instrument, the data of each monitoring station are deposited in the database of home server, form the water quality sample sequence, be convenient to water quality parameter is analyzed; The said good and bad water quality parameter of evaluating water quality that is used for comprises: pH, conductivity, turbidity, dissolved oxygen DO, temperature, ammonia nitrogen, chlorine residue and permanganate indices etc.;
(2) the water quality sample sequence being carried out normalization handles; Confirm the input variable of RBF neural network through calculating coefficient of autocorrelation; Sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction: the water quality sample sequence is for
Figure 515972DEST_PATH_IMAGE002
; According to
Figure 165260DEST_PATH_IMAGE004
data being carried out normalization handles; Wherein,
Figure 30447DEST_PATH_IMAGE006
and
Figure 169305DEST_PATH_IMAGE008
is respectively maximal value and minimum value in
Figure 690416DEST_PATH_IMAGE002
; The calculating of coefficient of autocorrelation is shown below:
Figure 940132DEST_PATH_IMAGE010
?,
In the formula;
Figure 364553DEST_PATH_IMAGE012
is autocorrelation function;
Figure 674312DEST_PATH_IMAGE014
is coefficient of autocorrelation to be asked;
Figure 682719DEST_PATH_IMAGE016
is the length of water quality time series
Figure 736126DEST_PATH_IMAGE002
; N is a water quality sample ordinal number, and
Figure 575906DEST_PATH_IMAGE018
is the water quality sample;
The scope of coefficient of autocorrelation is ; Set correlation coefficient threshold ; During as ; is so the input vector of RBF neural network
Figure 863536DEST_PATH_IMAGE028
is configured to
Figure 908853DEST_PATH_IMAGE030
; Confirm the length
Figure 304062DEST_PATH_IMAGE032
of training sample according to forecasting problem actual demand and sample data characteristics; Obtain the inputoutput pair of network, sample data is converted into the standard dynamic sequence data layout of RBF neural metwork training prediction;
(3) with error criterion poor
Figure 878580DEST_PATH_IMAGE036
as objective function; Utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread; Obtain optimum forecast model: utilize the newrbe function to set up the RBF neural network, utilize the differential evolution algorithm search to confirm the optimal value of RBF neural network spreading coefficient spread; The differential evolution algorithm is a kind of global optimization method based on the population evolution of floating-point code; The minimum problem of promptly finding the solution
Figure 83296DEST_PATH_IMAGE038
; Wherein is spread, objective function
Figure 623179DEST_PATH_IMAGE040
be error criterion poor
Figure 882122DEST_PATH_IMAGE036
expression as shown in the formula:
Figure 636452DEST_PATH_IMAGE042
Wherein
Figure 800893DEST_PATH_IMAGE018
is original water quality sequence;
Figure 324278DEST_PATH_IMAGE044
is the water quality sequence of prediction, and
Figure 691805DEST_PATH_IMAGE032
is training sample length;
(4) real-time sampling water quality data with the optimum prediction model that step 3 obtains, adopt the method for single-point iteration to realize multi-step prediction, and evaluating water quality predicts the outcome the effect of realization early warning; If exceed normal range then warning immediately.
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