CN103745093A - Principal component analysis-extreme learning machine (PCA-ELM) based coal mine water inrush prediction method - Google Patents

Principal component analysis-extreme learning machine (PCA-ELM) based coal mine water inrush prediction method Download PDF

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CN103745093A
CN103745093A CN201310726176.0A CN201310726176A CN103745093A CN 103745093 A CN103745093 A CN 103745093A CN 201310726176 A CN201310726176 A CN 201310726176A CN 103745093 A CN103745093 A CN 103745093A
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mine water
water inrush
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coal mine
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赵作鹏
宋国娟
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China University of Mining and Technology CUMT
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Abstract

The invention relates to a PCA-ELM based coal mine water inrush prediction method and belongs to the field of coal mine water inrush prediction methods. The method comprises the steps of obtaining a plurality of data affecting coal mine water inrush in the coal mine normal exploitation running state; screening a plurality of factors affecting coal mine water inrush through the PCA to obtain a main controlling factor playing a decisive role in coal mine water inrush; partitioning sample data only containing the main controlling factor into a training set, a verification set and a testing set which are corresponding to model training, verification and testing respectively; building an ELM network model, and training the data through the ELM algorithm; verifying a coal mine water inrush prediction model through the verification data, beginning to rebuild a model from the PCA if the obtaining prediction result has no apparent advantages compared with other algorithms, and using the model as the prediction model for actually predicting the coal mine water inrush prediction condition if the prediction result is ideal.

Description

A kind of mine water inrush Forecasting Methodology based on PCA-EML
Technical field
The present invention relates to a kind of mine water inrush Forecasting Methodology, particularly a kind of mine water inrush Forecasting Methodology based on PCA-EML.
Background technology
Mine water inrush is one of major hidden danger in Safety of Coal Mine Production, correctly predicts in time gushing water, to ensureing having great importance smoothly of coal mining.
The factor that affects mine water inrush is a lot, and mostly these factors are ambiguity and fuzzy similarity between each influence factor, have complicated nonlinear relationship, are difficult to set up forecast model by classical mathematical theory.Based on this, experts and scholars have proposed the method for prediction water bursting in mine, there is the BP of employing algorithm to set up gushing water forecast neural network model based on water bursting in mine sample instance, mine water inrush Forecasting Methodology based on genetic neural network and water bursting in mine information being processed with support vector machine-rough set SVM-RS model, but the training speed of BP neural network is slow, easily be absorbed in local minimum point, SVM needs artificially to arrange kernel function in learning process, the parameters such as penalty coefficient, and need to consume a large amount of time carries out parameter adjustment, the mine water inrush Forecasting Methodology of extreme learning machine (ELM) algorithm in conjunction with principal component analysis (PCA) (PCA) therefore proposed.
ELM(extreme learning machine) algorithm is a kind of single hidden layer feedforward neural network learning algorithm, this algorithm does not need the biasing of the input weights memory hidden layer of adjusting network in training process, the hidden layer node number that network only need be set, just can produce unique optimum solution.Compared with traditional algorithm, this algorithm has that parameter is selected easily, pace of learning is fast and the advantage such as Generalization Capability is good.
PCA(principal component analysis (PCA)) method is the multivariate statistics technology of a kind of data compression and feature extraction, can effectively remove the correlativity between data, reduces the complexity of calculating.The influence factor of mine water inrush during as network input variable, comprises the information being relative to each other, certainly due to the not independence between network input variable in these influence factors, may cause information overlap, and then the complexity of increase network, reduce network performance, impact prediction precision.
Summary of the invention
The present invention seeks to provide the mine water inrush Forecasting Methodology based on PCA-EML that a kind of parameter is selected easily, pace of learning is fast and Generalization Capability is good.
To achieve these goals, the present invention is by the following technical solutions:
By principal component analysis (PCA), carry out the input parameter of optimization neural network, first utilize principal component analysis (PCA) to carry out pre-service to many factors data, eliminate the information overlap between legacy data, produce new separate training sample, retain as much as possible original information, then the input using the training sample of reconstruct as extreme learning machine, the structure complexity of reduction neural network, improves speed of convergence.
Concrete step is as follows:
(1) obtain colliery and normally exploit the numerous data that affect mine water inrush under running status;
(2) utilize principal component analysis (PCA) to screen the many factors that affects mine water inrush, obtain mine water inrush to rise the Dominated Factors of deciding factor;
The concrete steps of mine water inrush master control influence factor being screened by principal component analysis (PCA) are as follows:
1. get colliery and normally exploit the sampled data matrix under running status, and it is carried out to standardization obtain matrix Χ;
2. according to the data matrix Χ after standardization, calculate covariance matrix R;
3. according to covariance matrix R, obtain eigenwert, principal component contributor rate and accumulative total variance contribution ratio, determine major component number;
4. by major component number, determine load matrix W, W=XM, is the learning sample of the extreme learning machine model of foundation;
(3) sample data that only comprises Dominated Factors is divided into training set, checking collection and test set, corresponds respectively to training, checking and the test of model;
(4) set up extreme learning machine network model;
Set up extreme learning machine network model, and through extreme learning machine algorithm, these data are trained, its step is as follows:
1. given N training sample { x i, t i, i=1,2 ..., N; Initial hidden layer nodes is made as
Figure BDA0000446002580000021
excitation function g (x);
2. be input weights ω iwith threshold value b irandom assignment, wherein
Figure BDA0000446002580000022
3. according to the unified model formula of the SLFN of excitation function g (x) Σ i = 1 N ~ β i g ( x j ) = Σ i = 1 N ~ β i g ( w i · x j + b i ) = o j , j = 1 , . . . , N , Be expressed in matrix as: H β=T,
H ( w 1 , . . . , w N ~ , b 1 , . . . , b N ~ , x 1 , . . . , x N ~ ) = | g ( w 1 · x 1 + b 1 ) . . . g ( w N ~ · x 1 + b N ~ ) . . . . . . . . . g ( w 1 · x N + b 1 ) . . . g ( w N ~ · x N ~ + b N ~ ) | N × N ~ ,
β = | β T 1 . . . β N ~ T | N ~ × m , T = | t 1 T . . . t N T | N × m Calculate output matrix H; Wherein H is called the hidden layer output matrix of neural network; x i=[x i1, x i2... x in] t∈ R n, t i=[t i1, t i2... t im] t∈ R m, ω ifor connecting the input weights of i hidden layer node; β ifor connecting the output weights of i concealed nodes and output node; b iit is the deviation of i concealed nodes; w ix jrepresent w iand x jinner product; Excitation function g (x) can be " Sigmiod ", " Sine " or RBF etc.
4. calculate output weights β: β=H +y, wherein H +for the Moore-Penrose generalized inverse of hidden layer output matrix H.
(5) utilize verification msg checking mine water inrush forecast model, if what obtain predicts the outcome and other algorithm comparisons, there is no clear superiority, from principal component analysis (PCA), start to re-establish model, comparatively desirable if predict the outcome, set it as forecast model and carry out actual prediction mine water inrush situation.
Beneficial effect, due to the mine water inrush Forecasting Methodology that has adopted principal component analysis (PCA) and extreme learning machine method to combine, principal component analysis (PCA) is predicted for mine water inrush with the extreme learning machine method method that combines, take mine water inrush historical data as sample, by principal component analysis (PCA), input data are compressed, reduced input variable dimension, remove mine water inrush is affected to the little factor, reduce to a certain extent the error that redundant data causes, improved model prediction precision; The mine water inrush forecast model of structure based on principal component analysis (PCA)-extreme learning machine, improved the independence between nerve network input parameter, reduce the complexity of network, improve network performance and precision of prediction, simultaneously, reduce the structure complexity of neural network, improve speed of convergence, met well the real-time of mine water inrush prediction.
Accompanying drawing explanation
Fig. 1 is the mine water inrush prediction process flow diagram based on PCA-ELM.
Embodiment
Embodiment 1: the input parameter that carrys out optimization neural network with principal component analysis (PCA), first utilize principal component analysis (PCA) to carry out pre-service to many factors data, eliminate the information overlap between legacy data, produce new separate training sample, retain as much as possible original information, then the input using the training sample of reconstruct as extreme learning machine, the structure complexity of reduction neural network, improves speed of convergence.
(1) obtain colliery and normally exploit the numerous data that affect mine water inrush under running status;
(2) utilize principal component analysis (PCA) to screen the many factors that affects mine water inrush, obtain mine water inrush to rise the Dominated Factors of deciding factor;
The concrete steps of mine water inrush master control influence factor being screened by principal component analysis (PCA) are as follows:
1. get colliery and normally exploit the sampled data matrix under running status, and it is carried out to standardization obtain matrix Χ;
2. according to the data matrix Χ after standardization, calculate covariance matrix R;
3. according to covariance matrix R, obtain eigenwert, principal component contributor rate and accumulative total variance contribution ratio, determine major component number;
4. by major component number, determine load matrix W, W=XM, is the learning sample of the extreme learning machine model of foundation.
(3) sample data that only comprises Dominated Factors is divided into training set, checking collection and test set, corresponds respectively to training, checking and the test of model;
(4) set up extreme learning machine network model;
Set up extreme learning machine network model, and through extreme learning machine algorithm, these data are trained, its step is as follows:
1. a given N training sample xi, ti}, i=1,2 ..., N; Initial hidden layer nodes is made as
Figure BDA0000446002580000031
excitation function g (x);
2. be input weights ω i and threshold value bi random assignment, wherein
Figure BDA0000446002580000032
3. according to formula Σ i = 1 N ~ β i g ( x j ) = Σ i = 1 N ~ β i g ( w i · x j + b i ) = o j , j = 1 , . . . , N Calculate output matrix H;
4. calculate output weights β: β=H +y, wherein, H +for the Moore-Penrose generalized inverse of hidden layer output matrix H.
(5) utilize verification msg checking mine water inrush forecast model, if what obtain predicts the outcome and other algorithm comparisons, there is no clear superiority, from principal component analysis (PCA), start to re-establish model, comparatively desirable if predict the outcome, set it as forecast model and carry out actual prediction mine water inrush situation.

Claims (3)

1. the mine water inrush Forecasting Methodology based on PCA-EML, it is characterized in that: the input parameter that carrys out optimization neural network by principal component analysis (PCA), first utilize principal component analysis (PCA) to carry out pre-service to many factors data, eliminate the information overlap between legacy data, produce new separate training sample, retain as much as possible original information, then the input using the training sample of reconstruct as extreme learning machine, reduce the structure complexity of neural network, improve speed of convergence, concrete steps are as follows:
(1) obtain colliery and normally exploit the numerous data that affect mine water inrush under running status;
(2) utilize principal component analysis (PCA) to screen the many factors that affects mine water inrush, obtain mine water inrush to rise the Dominated Factors of deciding factor;
(3) sample data that only comprises Dominated Factors is divided into training set, checking collection and test set, corresponds respectively to training, checking and the test of model;
(4) set up extreme learning machine network model;
(5) utilize verification msg checking mine water inrush forecast model, if what obtain predicts the outcome and other algorithm comparisons, there is no clear superiority, from principal component analysis (PCA), start to re-establish model, comparatively desirable if predict the outcome, set it as forecast model and carry out actual prediction mine water inrush situation.
2. the mine water inrush Forecasting Methodology based on PCA-EML according to claim 1, is characterized in that: described screens mine water inrush master control influence factor by principal component analysis (PCA), and its step is as follows:
(1) get colliery and normally exploit the sampled data matrix under running status, and it is carried out to standardization obtain matrix Χ;
(2) according to the data matrix Χ after standardization, calculate covariance matrix R;
(3) according to covariance matrix R, obtain eigenwert, principal component contributor rate and accumulative total variance contribution ratio, determine major component number;
(4) by major component number, determine load matrix W, W=XM, is the ELM(extreme learning machine of foundation) learning sample of model.
3. the mine water inrush Forecasting Methodology based on PCA-EML according to claim 1, is characterized in that: set up extreme learning machine network model, and through extreme learning machine algorithm, these data are trained, its step is as follows:
(1) given N training sample { x i, t i, i=1,2 ..., N; Initial hidden layer nodes is made as
Figure FDA0000446002570000011
excitation function g (x);
(2) be input weights ω iwith threshold value b irandom assignment, wherein
Figure FDA0000446002570000012
(3) according to the unified model formula of the SLFN of excitation function g (x)
Σ i = 1 N ~ β i g ( x j ) = Σ i = 1 N ~ β i g ( w i · x j + b i ) = o j , j = 1 , . . . , N ,
Be expressed in matrix as
:Hβ=T, H ( w 1 , . . . , w N ~ , b 1 , . . . , b N ~ , x 1 , . . . , x N ~ ) = | g ( w 1 · x 1 + b 1 ) . . . g ( w N ~ · x 1 + b N ~ ) . . . . . . . . . g ( w 1 · x N + b 1 ) . . . g ( w N ~ · x N ~ + b N ~ ) | N × N ~ ,
β = | β T 1 . . . β N ~ T | N ~ × m , T = | t 1 T . . . t N T | N × m Calculate output matrix H; Wherein H is called the hidden layer output matrix of neural network; x i=[x i1, x i2... x in] t∈ R n, t i=[t i1, t i2... t im] t∈ R m, ω ifor connecting the input weights of i hidden layer node; β ifor connecting the output weights of i concealed nodes and output node; b iit is the deviation of i concealed nodes; w ix jrepresent w iand x jinner product; Excitation function g (x) can be " Sigmiod ", " Sine " or RBF;
(4) calculate output weights β: β=H +y, wherein H +for the Moore-Penrose generalized inverse of hidden layer output matrix H.
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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104070083A (en) * 2014-06-27 2014-10-01 东北大学 Method for measuring rotating speed of guiding disc of perforating machine based on integrated PCA-ELM (Principal Component Analysis)-(Extrem Learning Machine) method
CN104156560A (en) * 2014-07-12 2014-11-19 中国矿业大学 Multi-level coal mine water inrush prediction method based on SaE-ELM (self-adaptive evolutionary extreme learning machine)
CN106096543A (en) * 2016-06-08 2016-11-09 东华大学 A kind of Handwritten Digit Recognition method based on modified extreme learning machine
CN106971073A (en) * 2017-03-28 2017-07-21 安徽理工大学 A kind of identification of nonlinearity method at water bursting in mine water source
CN107122861A (en) * 2017-04-28 2017-09-01 辽宁工程技术大学 A kind of Forecast of Gas Emission method based on PCA PSO ELM
CN107526117A (en) * 2017-07-06 2017-12-29 天津科技大学 SVEL Forecasting Methodology based on autocoding and the study joint network that transfinites
CN107696034A (en) * 2017-09-30 2018-02-16 东北大学 A kind of wrong autonomous restoration methods for industrial robot
CN107729716A (en) * 2017-11-27 2018-02-23 西安建筑科技大学 A kind of mine water inrush Forecasting Methodology based on long Memory Neural Networks in short-term
CN108596391A (en) * 2018-04-26 2018-09-28 南京英诺森软件科技有限公司 A kind of prediction and evaluation method of electricity power enterprise's equipment inventory spare unit quantity
CN108805357A (en) * 2018-06-13 2018-11-13 安徽理工大学 A kind of Fisher discrimination model water bursting source prediction techniques based on PCA analyses
CN110348639A (en) * 2019-07-16 2019-10-18 中国石油大学(华东) A kind of coal mine roof plate gushing water danger classes prediction technique
CN112684363A (en) * 2020-12-18 2021-04-20 北京工业大学 Lithium ion battery health state estimation method based on discharge process

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101775996A (en) * 2010-01-05 2010-07-14 中国矿业大学 Method for positioning, playing, real-time monitoring and early warning of hidden troubles of coal mine

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101775996A (en) * 2010-01-05 2010-07-14 中国矿业大学 Method for positioning, playing, real-time monitoring and early warning of hidden troubles of coal mine

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
彭慧芳,等.: "基于主成分分析法的底板突水危险性评价", 《煤矿开采》, vol. 17, no. 6, 31 December 2012 (2012-12-31), pages 101 - 104 *
李培,等.: "基于PCA-ELM的煤矿突水预测方法研究", 《工矿自动化》, vol. 39, no. 9, 30 September 2013 (2013-09-30), pages 46 - 49 *
鲁金涛,等.: "基于主成分分析与Fisher判别分析法的矿井突水水源识别方法", 《中国安全科学学报》, vol. 22, no. 7, 31 July 2012 (2012-07-31), pages 109 - 115 *

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CN104070083A (en) * 2014-06-27 2014-10-01 东北大学 Method for measuring rotating speed of guiding disc of perforating machine based on integrated PCA-ELM (Principal Component Analysis)-(Extrem Learning Machine) method
CN104156560A (en) * 2014-07-12 2014-11-19 中国矿业大学 Multi-level coal mine water inrush prediction method based on SaE-ELM (self-adaptive evolutionary extreme learning machine)
CN106096543A (en) * 2016-06-08 2016-11-09 东华大学 A kind of Handwritten Digit Recognition method based on modified extreme learning machine
CN106971073A (en) * 2017-03-28 2017-07-21 安徽理工大学 A kind of identification of nonlinearity method at water bursting in mine water source
CN107122861A (en) * 2017-04-28 2017-09-01 辽宁工程技术大学 A kind of Forecast of Gas Emission method based on PCA PSO ELM
CN107526117B (en) * 2017-07-06 2019-08-13 天津科技大学 Based on autocoding and transfinites and learn the acoustic speed prediction technique of joint network
CN107526117A (en) * 2017-07-06 2017-12-29 天津科技大学 SVEL Forecasting Methodology based on autocoding and the study joint network that transfinites
CN107696034A (en) * 2017-09-30 2018-02-16 东北大学 A kind of wrong autonomous restoration methods for industrial robot
CN107729716A (en) * 2017-11-27 2018-02-23 西安建筑科技大学 A kind of mine water inrush Forecasting Methodology based on long Memory Neural Networks in short-term
CN107729716B (en) * 2017-11-27 2020-10-27 西安建筑科技大学 Coal mine water inrush prediction method based on long-time and short-time memory neural network
CN108596391A (en) * 2018-04-26 2018-09-28 南京英诺森软件科技有限公司 A kind of prediction and evaluation method of electricity power enterprise's equipment inventory spare unit quantity
CN108805357A (en) * 2018-06-13 2018-11-13 安徽理工大学 A kind of Fisher discrimination model water bursting source prediction techniques based on PCA analyses
CN110348639A (en) * 2019-07-16 2019-10-18 中国石油大学(华东) A kind of coal mine roof plate gushing water danger classes prediction technique
CN112684363A (en) * 2020-12-18 2021-04-20 北京工业大学 Lithium ion battery health state estimation method based on discharge process

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