CN109946424A - Demarcate Gas classification method and system based on artificial bee colony and neural network - Google Patents
Demarcate Gas classification method and system based on artificial bee colony and neural network Download PDFInfo
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Abstract
The present invention discloses a kind of Demarcate Gas classification method based on artificial bee colony and neural network, comprising: obtains gas-monitoring data；Gas-monitoring data are subjected to cutting processing according to the period, obtain the gas data collection of each period；Judge the outlier that the gas data is concentrated, all outliers are converted into normal data value, null value point and over range point that gas data is concentrated are removed, the data set and normal data value after the null value point and over range point concentrated by removal gas data form new gas data collection；The neural network classification model is optimized using artificial bee colony algorithm, under test gas monitoring data are input in accurately neural network classification model, obtains gas classification as a result, demarcating according to classification results to gas.By means of the present invention and system, artificial bee colony algorithm and neural network algorithm are be combined with each other, can accurately predict in gas whether the trend containing pernicious gas, prediction result is accurate.
Description
Technical field
The present invention relates to big data field of artificial intelligence more particularly to a kind of based on artificial bee colony and neural network
Demarcate Gas classification method and system.
Background technique
With the development of industrialization science and technology, monitored fine of existing gas discharge, and traditional monitoring all passes through
After gas collecting, then by manually calculating, just know comprising how many kinds of gas in gas, and the type of these gases is
How, such measuring and calculating mode can only be obtained according to emission result, without can be carried out prediction, the more gesture of the gaseous species of discharge
It must pollute the environment, then the environment of pollution is cleared up again, will cause the waste of resource.The present invention be directed to this measuring and calculating
It improves, in conjunction with big data artificial intelligence mode, classification results is calculated to gas in advance, achieve the purpose that prediction.
Summary of the invention
The shortcomings that present invention is directed in the prior art, it is harmful to provide a kind of monitoring based on artificial bee colony and neural network
Gas prediction technique and system.
In order to solve the above-mentioned technical problem, the present invention is addressed by following technical proposals:
A kind of Demarcate Gas classification method based on artificial bee colony and neural network, comprising the following steps:
The gas-monitoring data for obtaining several gas-monitoring points, the gas-monitoring data got are multiple types
The data of gas include at least monoatomic gas, diatomic gas, three atomic gas and polymolecular gas；
Gas-monitoring data are subjected to cutting processing according to the period, obtain the gas data collection of each period；
Judge the outlier that the gas data is concentrated and the number for counting outlier, all outliers are converted to normally
Data value, removal gas data concentrate null value point and over range point, by removal gas data concentrate null value point and
Data set after over range point and normal data value form new gas data collection, and by new gas data collection be divided into training set and
Sample set；
Neural network classification model is established based on the sample set, using artificial bee colony algorithm to the neural network classification
Model optimizes, the neural network classification model after being optimized, and using training set to the neural network classification after optimization
Model is verified, and the accurate neural network classification model in error range is judged whether it is, and under test gas monitoring data are defeated
Enter into accurately neural network classification model, obtains gas classification as a result, demarcating according to classification results to gas.
As an embodiment, the gas-monitoring data for obtaining several gas-monitoring points are by respective numbers
Gas sensor get.
As an embodiment, the outlier in the judgement data set is known using Kalman filtering algorithm
Outlier in other data set, specific steps are as follows:
Assuming that Posterior probability distribution p (xk-1|y1:k-1) it is Gaussian Profile, then the system mode at k moment is expressed as xk=Axk-1
+Buk-1+qk-1, the measured value at k moment is expressed as yk=Hxk+rk, wherein ukIt is control amount of the k moment to system, uk-1It is k-1
Moment, A was the parameter matrix of the system mode at system k-1 moment, and B is the control amount at system k-1 moment to the control amount of system
Parameter matrix, H be the system k moment system mode parameter matrix, qk-1Indicate process noise, rkIt indicates measurement noise, uses
Qk-1Indicate process noise qk-1With system mode xkCovariance matrix, RkIndicate measurement noise rkWith measured value ykCovariance
Matrix；
System is updated according to the measurement moment, passes through the system mode of last momentTo update current time
The system mode of system mode, current time is expressed as:Determine the system mode at current time,
In,Indicate the system mode at current time, A is the parameter matrix of the system mode at system k-1 moment, and B is the system k-1 moment
Control amount parameter matrix, uk-1It is the k-1 moment to the control amount of system,For the system mode of last moment；
The error covariance p of last moment is obtained by the system mode of the last momentk-1With process noise qkAssociation
Variance matrix Q, and according to the error covariance p of last momentk-1With process noise qkCovariance matrix Q predict new mistake
DifferenceNew error is expressed as:Wherein, pk-1Indicate that error covariance, A indicate system parameter matrix,
Q indicates the covariance matrix of process noise, and T is mathematic sign, representing matrix transposition；
Pass through new errorTo the kalman gain K of current time systemkIt is updated, kalman gain indicates are as follows:Wherein, H is parameter matrix,Indicate new error, R indicates noise rkCovariance square
Battle array, T is mathematic sign, representing matrix transposition；
Pass through the kalman gain K of updatekUpdate, the system at current time are corrected to the system mode at current time
State is expressed as: For the system mode at the current time after Kalman filtering, whereinIndicate the system mode at current time, H expression parameter matrix, KkIndicate kalman gain,Be expressed as last moment is
System state,It is expressed as newly ceasing, when data are normal, innovation sequence is white noise sequence, and mean value 0 is new to cease
Variance isAt this point,D is r times of new breath mean square deviation, is configured to r, when new breath
When variance is more than criterion D, then current point is outlier, counts the number of outlier, and removal outlier inserts normal data, shape
At new gas data collection L；
Further include correction renewal process: the error of current time system mode is expressed as,This mistake
Journey is correction renewal process, pkThe error of current time system mode as Jing Guo Kalman filtering, during next
The error of system mode as last moment uses.
As an embodiment, the removal outlier, which inserts normal data, is filled out using Kalman filtering algorithm
Enter normal data.
It is as an embodiment, described that neural network classification model, specific steps are established based on the sample set are as follows:
New gas data collection L is subjected to data sectional with the sliding window for having overlapping, by before each section 90% data
Data with rear 10% carry out cutting, form the input data and output data of training dataset；
Neural network classification model is constructed by training dataset, neural network classification model isWherein, n is the number of plies of neural network classification model, and W indicates hidden layer weight matrix, matrix
Line number be each layer of neuron number, columns be the hidden layer weight matrix of input individual amount,It is defeated for each layer
Outgoing vector, p are input vector, and f is activation primitive.
As an embodiment, described excellent to neural network classification model progress using artificial bee colony algorithm
Change, the neural network classification model after being optimized, specifically:
Sample data is selected based on sample set, the input layer and middle layer, centre of neural network classification model is randomly generated
The connection weight W of layer and output layerij, Wjk；
The unit reality output vector of selected sample data is obtained by following formulaFormula are as follows:Wherein, IjFor the input of intermediate hiding node layer j, WijFor weight,For unit reality output vector,
θjIt indicates to change the active threshold value of unit j, unit reality output vector
It is calculated by the following formula the value number E of sample data reality output vector Yu desired value difference quadratic sum, if value
Number is not more than preset error amount, then neural network classification model training terminates, if value number is greater than error amount, calculates input
Layer and the weight adjusted value and adjusting thresholds value, formula of middle layer, middle layer and output layer areIts
In,Indicate the output vector of unit k, TkIndicate the desired output of output layer unit k；
New connection weight and threshold value are recalculated, according to new weight and sample data, recalculates the reality of sample
Output vectorWith value number, if value number is not more than preset error amount, neural network classification model training terminates, if valence
Be worth number and be greater than error amount, then regard weight and threshold value as the initial solution of artificial bee colony, set initial parameter, will be worth number as with
The target value of lower formula, formula are as follows:Wherein, h is the objective function of optimization problem,Middle hiGreater than 0,1+
In abs (hi), hiLess than 0；
Artificial bee colony algorithm is called, optimal solution is sought, the optimal weights and threshold value generated according to artificial bee colony algorithm are as mind
Next time trained initial weight and threshold value through network class model retrieves input layer and middle layer, middle layer and defeated
The weight adjusted value and adjusting thresholds value of layer out；
Neural network classification model training terminates, the neural network classification model after being optimized.
As an embodiment, described and use training set tests the neural network classification model after optimization
Card, judges whether it is the accurate neural network classification model in error range, specifically:
The neural network classification model is trained with the method for error back propagation based on training set, repetition changes
In generation, restrains until the vector b of matrix W, then the neural network classification model has trained, and has obtained accurately nerve net at this time
Network disaggregated model.
As an embodiment, described to obtain gas classification as a result, demarcating according to classification results to gas, have
Body are as follows:
When needing the Future Data to a time point to predict, the data for the previous period of current point in time are taken
Accurately neural network classification model is inputted, neural network classification model exports the prediction data of following a period of time, according to pre-
Measured data obtains classification results；
It is demarcated by type of the classification results to gas, whether normal predicts the following data monitored.
A kind of Demarcate Gas categorizing system based on artificial bee colony and neural network, including at data capture unit, data
Manage unit, data reprocessing unit, model foundation and predicting unit；
The data capture unit, for obtaining the gas-monitoring data of several gas-monitoring points, what is got is described
Gas-monitoring data are the data of multiple types gas, include at least monoatomic gas, diatomic gas, three atomic gas and more
Molecular gas；
The data processing unit, for gas-monitoring data to be carried out cutting processing according to the period, when obtaining each
Between section gas data collection；
The data reprocess unit, for judging the outlier of the gas data concentration and counting a of outlier
All outliers are converted to normal data value by number, and the null value point and over range point that removal gas data is concentrated pass through removal
Data set and normal data value after null value point and over range point that gas data is concentrated form new gas data collection, and will
New gas data collection is divided into training set and sample set；
The model foundation and predicting unit, for establishing neural network classification model based on the sample set, using people
Work ant colony algorithm optimizes the neural network classification model, the neural network classification model after being optimized, and uses
Training set verifies the neural network classification model after optimization, judges whether it is the accurate neural network point in error range
Under test gas monitoring data are input in accurately neural network classification model by class model, obtain gas classification as a result, according to
Classification results demarcate gas.
The present invention is due to using above technical scheme, with significant technical effect:
By means of the present invention and system, artificial bee colony algorithm and neural network algorithm are be combined with each other, it can be accurate
The gaseous species for including in gas are predicted, prediction result is accurate.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is overall flow schematic diagram of the invention；
Fig. 2 is total system schematic diagram of the invention.
Specific embodiment
The present invention will be further described in detail below with reference to the embodiments, following embodiment be explanation of the invention and
The invention is not limited to following embodiments.
Embodiment 1:
A kind of Demarcate Gas classification method based on artificial bee colony and neural network, comprising the following steps:
The gas-monitoring data for obtaining several gas-monitoring points, the gas-monitoring data got are multiple types
The data of gas include at least monoatomic gas, diatomic gas, three atomic gas and polymolecular gas；
Gas-monitoring data are subjected to cutting processing according to the period, obtain the gas data collection of each period；
Judge the outlier that the gas data is concentrated and the number for counting outlier, all outliers are converted to normally
Data value, removal gas data concentrate null value point and over range point, by removal gas data concentrate null value point and
Data set after over range point and normal data value form new gas data collection, and by new gas data collection be divided into training set and
Sample set；
Neural network classification model is established based on the sample set, using artificial bee colony algorithm to the neural network classification
Model optimizes, the neural network classification model after being optimized, and using training set to the neural network classification after optimization
Model is verified, and the accurate neural network classification model in error range is judged whether it is, and under test gas monitoring data are defeated
Enter into accurately neural network classification model, obtains gas classification as a result, demarcating according to classification results to gas.
In the training of neural network classification model, the power that artificial bee colony algorithm carrys out optimization neural network disaggregated model is introduced
It is worth threshold value etc., the prediction result precision and convergence rate of neural network classification model can be accurately improved, so that prediction result
It is more accurate.
More preferably specifically, the gas-monitoring data for obtaining several gas-monitoring points are passed by the gas of respective numbers
What sensor was got.
In step S300, the outlier in the judgement data set is to identify data using Kalman filtering algorithm
The outlier of concentration, specific steps are as follows:
Assuming that Posterior probability distribution p (xk-1|y1:k-1) it is Gaussian Profile, then the system mode at k moment is expressed as xk=Axk-1
+Buk-1+qk-1, the measured value at k moment is expressed as yk=Hxk+rk, wherein ukIt is control amount of the k moment to system, uk-1It is k-1
Moment, A was the parameter matrix of the system mode at system k-1 moment, and B is the control amount at system k-1 moment to the control amount of system
Parameter matrix, H be the system k moment system mode parameter matrix, qk-1Indicate process noise, rkIt indicates measurement noise, uses
Qk-1Indicate process noise qk-1With system mode xkCovariance matrix, RkIndicate measurement noise rkWith measured value ykCovariance
Matrix；
System is updated according to the measurement moment, passes through the system mode of last momentTo update current time
The system mode of system mode, current time is expressed as:Determine the system mode at current time,
In,Indicate the system mode at current time, A is the parameter matrix of the system mode at system k-1 moment, and B is the system k-1 moment
Control amount parameter matrix, uk-1It is the k-1 moment to the control amount of system,For the system mode of last moment；
The error covariance p of last moment is obtained by the system mode of the last momentk-1With process noise qkAssociation
Variance matrix Q, and according to the error covariance p of last momentk-1With process noise qkCovariance matrix Q predict new mistake
DifferenceNew error is expressed as:Wherein, pk-1Indicate that error covariance, A indicate system parameter square
Battle array, Q indicate the covariance matrix of process noise, and T is mathematic sign, representing matrix transposition；
Pass through new errorTo the kalman gain K of current time systemkIt is updated, kalman gain indicates are as follows:Wherein, H is parameter matrix,Indicate new error, R indicates noise rkCovariance square
Battle array, T is mathematic sign, representing matrix transposition；
Pass through the kalman gain K of updatekUpdate, the system at current time are corrected to the system mode at current time
State is expressed as: For the system mode at the current time after Kalman filtering, whereinIndicate the system mode at current time, H expression parameter matrix, KkIndicate kalman gain,Be expressed as last moment is
System state,It is expressed as newly ceasing, when data are normal, innovation sequence is white noise sequence, and mean value 0 is new to cease
Variance isAt this point,D is r times of new breath mean square deviation, is configured to r, when new breath
When variance is more than criterion D, then current point is outlier, counts the number of outlier, and removal outlier inserts normal data, shape
At new gas data collection L；
Further include correction renewal process: the error of current time system mode is expressed as,This mistake
Journey is correction renewal process, pkThe error of current time system mode as Jing Guo Kalman filtering, during next
The error of system mode as last moment uses.
Kalman filtering is a kind of using linear system state equation, data is observed by system input and output, to system
The algorithm of state progress optimal estimation.Due to including the influence of the noise in system and interference in observation data, so optimal estimate
Meter is also considered as filtering.It is also sent a telegraph by Kalman filtering algorithm removal, it is as a result more accurate, so that obtained data
Collect that miscellaneous point is small, the later period is used for training meeting so that the model at trained is more accurate.So in the present embodiment, the removal is wild
It is to insert normal data using Kalman filtering algorithm that value point, which inserts normal data,.
It is described that neural network classification model, specific steps are established based on the sample set are as follows:
New gas data collection L is subjected to data sectional with the sliding window for having overlapping, by before each section 90% data
Data with rear 10% carry out cutting, form the input data and output data of training dataset；
Neural network classification model is constructed by training dataset, neural network classification model isWherein, n is the number of plies of neural network classification model, and W indicates hidden layer weight matrix, matrix
Line number be each layer of neuron number, columns be the hidden layer weight matrix of input individual amount,It is defeated for each layer
Outgoing vector, p are input vector, and f is activation primitive.
It is described that the neural network classification model is optimized using artificial bee colony algorithm in step S400, it obtains
Neural network classification model after optimization, specifically:
Sample data is selected based on sample set, the input layer and middle layer, centre of neural network classification model is randomly generated
The connection weight W of layer and output layerij, Wjk；
The unit reality output vector of selected sample data is obtained by following formulaFormula are as follows:Wherein, IjFor the input of intermediate hiding node layer j, WijFor weight,For unit reality output vector,
θjIt indicates to change the active threshold value of unit j, unit reality output vector
It is calculated by the following formula the value number E of sample data reality output vector Yu desired value difference quadratic sum, if value
Number is not more than preset error amount, then neural network classification model training terminates, if value number is greater than error amount, calculates input
Layer and the weight adjusted value and adjusting thresholds value, formula of middle layer, middle layer and output layer areIts
In,Indicate the output vector of unit k, TkIndicate the desired output of output layer unit k；
New connection weight and threshold value are recalculated, according to new weight and sample data, recalculates the reality of sample
Output vectorWith value number, if value number is not more than preset error amount, neural network classification model training terminates, if valence
Be worth number and be greater than error amount, then regard weight and threshold value as the initial solution of artificial bee colony, set initial parameter, will be worth number as with
The target value of lower formula, formula are as follows:Wherein, h is the objective function of optimization problem,Middle hiGreater than 0,1
In+abs (hi), hiLess than 0；
Artificial bee colony algorithm is called, optimal solution is sought, the optimal weights and threshold value generated according to artificial bee colony algorithm are as mind
Next time trained initial weight and threshold value through network class model retrieves input layer and middle layer, middle layer and defeated
The weight adjusted value and adjusting thresholds value of layer out；
Neural network classification model training terminates, the neural network classification model after being optimized.
As an embodiment, described and use training set tests the neural network classification model after optimization
Card, judges whether it is the accurate neural network classification model in error range, specifically:
The neural network classification model is trained with the method for error back propagation based on training set, repetition changes
In generation, restrains until the vector b of matrix W, then the neural network classification model has trained, and has obtained accurately nerve net at this time
Network disaggregated model.
It is more specifically, described to obtain gas classification as a result, demarcating according to classification results to gas, specifically:
When needing the Future Data to a time point to predict, the data for the previous period of current point in time are taken
Accurately neural network classification model is inputted, neural network classification model exports the prediction data of following a period of time, according to pre-
Measured data obtains classification results；
It is demarcated by type of the classification results to gas, whether normal predicts the following data monitored.
Embodiment 2:
A kind of Demarcate Gas categorizing system based on artificial bee colony and neural network, including data capture unit 100, data
Processing unit 200, data reprocessing unit 300, model foundation and predicting unit 400；
The data capture unit 100, for obtaining the gas-monitoring data of several gas-monitoring points, the institute got
State gas-monitoring data be multiple types gas data, include at least monoatomic gas, diatomic gas, three atomic gas and
Polymolecular gas；
The data processing unit 200 obtains each for gas-monitoring data to be carried out cutting processing according to the period
The gas data collection of period；
The data reprocess unit 300, for judging the outlier of the gas data concentration and counting outlier
All outliers are converted to normal data value, the null value point and over range point that removal gas data is concentrated, by going by number
The data set and the new gas data collection of normal data value composition after null value point and over range point in degasification volumetric data set, and
New gas data collection is divided into training set and sample set；
The model foundation and predicting unit 400 are used for establishing neural network classification model based on the sample set
Artificial bee colony algorithm optimizes the neural network classification model, the neural network classification model after being optimized, and adopts
The neural network classification model after optimization is verified with training set, judges whether it is the accurate neural network in error range
Under test gas monitoring data are input in accurately neural network classification model by disaggregated model, obtain gas classification as a result, root
Gas is demarcated according to classification results.
System through the invention, artificial bee colony algorithm and neural network algorithm are be combined with each other, and can accurately be predicted
The gaseous species for including in gas, prediction result are accurate.
In addition, it should be noted that, the specific embodiments described in this specification, the shape of parts and components are named
Title etc. can be different.The equivalent or simple change that all structure, feature and principles described according to the invention patent design are done, is wrapped
It includes in the scope of protection of the patent of the present invention.Those skilled in the art can be to described specific implementation
Example is done various modifications or additions or is substituted in a similar manner, and without departing from structure of the invention or surmounts this
Range as defined in the claims, is within the scope of protection of the invention.
Claims (9)
1. a kind of Demarcate Gas classification method based on artificial bee colony and neural network, it is characterised in that the following steps are included:
The gas-monitoring data for obtaining several gas-monitoring points, the gas-monitoring data got are multiple types gas
Data, include at least monoatomic gas, diatomic gas, three atomic gas and polymolecular gas；
Gas-monitoring data are subjected to cutting processing according to the period, obtain the gas data collection of each period；
Judge the outlier that the gas data is concentrated and the number for counting outlier, all outliers are converted into normal data
Value, the null value point and over range point that removal gas data is concentrated, the null value point and super model concentrated by removal gas data
Data set and normal data value after enclosing a little form new gas data collection, and new gas data collection is divided into training set and sample
Collection；
Neural network classification model is established based on the sample set, using artificial bee colony algorithm to the neural network classification model
It optimizes, the neural network classification model after being optimized, and using training set to the neural network classification model after optimization
It is verified, judges whether it is the accurate neural network classification model in error range, under test gas monitoring data are input to
Accurately in neural network classification model, gas classification is obtained as a result, demarcating according to classification results to gas.
2. the Demarcate Gas classification method according to claim 1 based on artificial bee colony and neural network, which is characterized in that
The gas-monitoring data for obtaining several gas-monitoring points are got by the gas sensor of respective numbers.
3. the Demarcate Gas classification method according to claim 1 based on artificial bee colony and neural network, which is characterized in that
Outlier in the judgement data set is using the outlier in Kalman filtering algorithm identification data set, specific steps
Are as follows:
Assuming that Posterior probability distribution p (xk-1|y1:k-1) it is Gaussian Profile, then the system mode at k moment is expressed as xk=Axk-1+
Buk-1+qk-1, the measured value at k moment is expressed as yk=Hxk+rk, wherein ukIt is control amount of the k moment to system, uk-1When being k-1
The control amount to system is carved, A is the parameter matrix of the system mode at system k-1 moment, and B is the control amount at system k-1 moment
Parameter matrix, H are the parameter matrix of the system mode at system k moment, qk-1Indicate process noise, rkIt indicates measurement noise, uses
Qk-1Indicate process noise qk-1With system mode xkCovariance matrix, RkIndicate measurement noise rkWith measured value ykCovariance
Matrix；
System is updated according to the measurement moment, passes through the system mode of last momentTo update the system shape at current time
The system mode of state, current time is expressed as:Determine the system mode at current time, whereinTable
Show the system mode at current time, A is the parameter matrix of the system mode at system k-1 moment, and B is the control at system k-1 moment
The parameter matrix of amount, uk-1It is the k-1 moment to the control amount of system,For the system mode of last moment；
The error covariance p of last moment is obtained by the system mode of the last momentk-1With process noise qkCovariance
Matrix Q, and according to the error covariance p of last momentk-1With process noise qkCovariance matrix Q predict new error
New error is expressed as:Wherein, pk-1Indicate that error covariance, A indicate that system parameter matrix, Q indicate
The covariance matrix of process noise, T are mathematic sign, representing matrix transposition；
Pass through new errorTo the kalman gain K of current time systemkIt is updated, kalman gain indicates are as follows:Wherein, H is parameter matrix,Indicate new error, R indicates noise rkCovariance square
Battle array, T is mathematic sign, representing matrix transposition；
Pass through the kalman gain K of updatekUpdate, the system mode at current time are corrected to the system mode at current time
It indicates are as follows: For the system mode at the current time after Kalman filtering, wherein
Indicate the system mode at current time, H expression parameter matrix, KkIndicate kalman gain,The system for being expressed as last moment
State,It is expressed as newly ceasing, when data are normal, innovation sequence is white noise sequence, mean value 0, new breath side
Difference isAt this point,D is r times of new breath mean square deviation, is configured to r, when new breath side
When difference is more than criterion D, then current point is outlier, counts the number of outlier, and removal outlier is inserted normal data, formed
New gas data collection L；
Further include correction renewal process: the error of current time system mode is expressed as,This process is school
Positive renewal process, pkThe error of current time system mode as Jing Guo Kalman filtering, as upper during next
The error of the system mode at one moment uses.
4. the Demarcate Gas classification method according to claim 3 based on artificial bee colony and neural network, which is characterized in that
It is to insert normal data using Kalman filtering algorithm that the removal outlier, which inserts normal data,.
5. the Demarcate Gas classification method according to claim 1 based on artificial bee colony and neural network, which is characterized in that
It is described that neural network classification model, specific steps are established based on the sample set are as follows:
New gas data collection L is subjected to data sectional with the sliding window for having overlapping, by before each section 90% data with after
10% data carry out cutting, form the input data and output data of training dataset；
Neural network classification model is constructed by training dataset, neural network classification model isWherein, n is the number of plies of neural network classification model, and W indicates hidden layer weight matrix, matrix
Line number be each layer of neuron number, columns be the hidden layer weight matrix of input individual amount,It is defeated for each layer
Outgoing vector, p are input vector, and f is activation primitive.
6. the Demarcate Gas classification method according to claim 1 based on artificial bee colony and neural network, which is characterized in that
It is described that the neural network classification model is optimized using artificial bee colony algorithm, the neural network classification mould after being optimized
Type, specifically:
Select sample data based on sample set, be randomly generated neural network classification model input layer and middle layer, middle layer and
The connection weight W of output layerij, Wjk；
The unit reality output vector of selected sample data is obtained by following formulaFormula are as follows:Its
In, IjFor the input of intermediate hiding node layer j, WijFor weight,For unit reality output vector, θjIndicate that changing unit j lives
The threshold value of property, unit reality output vector
It is calculated by the following formula the value number E of sample data reality output vector Yu desired value difference quadratic sum, if value number is not
Greater than preset error amount, then neural network classification model training terminates, if value number be greater than error amount, calculate input layer and
The weight adjusted value and adjusting thresholds value, formula of middle layer, middle layer and output layer beWherein,Indicate the output vector of unit k, TkIndicate the desired output of output layer unit k；
New connection weight and threshold value are recalculated, according to new weight and sample data, recalculates the reality output of sample
VectorWith value number, if value number is not more than preset error amount, neural network classification model training terminates, if value number
Greater than error amount, then using weight and threshold value as the initial solution of artificial bee colony, initial parameter is set, using value number as following public affairs
The target value of formula, formula are as follows:Wherein, h is the objective function of optimization problem,Middle hiGreater than 0,1+abs
(hi) in, hiLess than 0；
Artificial bee colony algorithm is called, optimal solution is sought, the optimal weights and threshold value generated according to artificial bee colony algorithm are as nerve net
The next time trained initial weight and threshold value of network disaggregated model, retrieves input layer and middle layer, middle layer and output layer
Weight adjusted value and adjusting thresholds value；
Neural network classification model training terminates, the neural network classification model after being optimized.
7. the Demarcate Gas classification method according to claim 1 based on artificial bee colony and neural network, which is characterized in that
Described and use training set verifies the neural network classification model after optimization, judges whether it is accurate in error range
Neural network classification model, specifically:
The neural network classification model is trained with the method for error back propagation based on training set, iteration is straight
Vector b to matrix W restrains, then the neural network classification model has trained, and has obtained accurately neural network point at this time
Class model.
8. the Demarcate Gas classification method according to claim 7 based on artificial bee colony and neural network, which is characterized in that
It is described to obtain gas classification as a result, demarcating according to classification results to gas, specifically:
When needing the Future Data to a time point to predict, the data input for the previous period of current point in time is taken
Accurately neural network classification model, neural network classification model export the prediction data of following a period of time, according to prediction number
According to obtaining classification results；
It is demarcated by type of the classification results to gas, whether normal predicts the following data monitored.
9. a kind of Demarcate Gas categorizing system based on artificial bee colony and neural network, which is characterized in that including data acquisition list
Member, data processing unit, data reprocessing unit, model foundation and predicting unit；
The data capture unit, for obtaining the gas-monitoring data of several gas-monitoring points, the gas got
Monitoring data are the data of multiple types gas, include at least monoatomic gas, diatomic gas, three atomic gas and polymolecular
Gas；
The data processing unit obtains each period for gas-monitoring data to be carried out cutting processing according to the period
Gas data collection；
The data reprocess unit, will for judging outlier that the gas data is concentrated and the number for counting outlier
All outliers are converted to normal data value, the null value point and over range point that removal gas data is concentrated, by removing gas
Data set after null value point and over range point and normal data value in data set form new gas data collection, and by new gas
Volumetric data set is divided into training set and sample set；
The model foundation and predicting unit, for establishing neural network classification model based on the sample set, using artificial bee
Group's algorithm optimizes the neural network classification model, the neural network classification model after being optimized, and using training
Collection verifies the neural network classification model after optimization, judges whether it is the accurate neural network classification mould in error range
Under test gas monitoring data are input in accurately neural network classification model by type, obtain gas classification as a result, according to classification
As a result gas is demarcated.
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