CN106355540A - Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network - Google Patents
Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network Download PDFInfo
- Publication number
- CN106355540A CN106355540A CN201610830410.8A CN201610830410A CN106355540A CN 106355540 A CN106355540 A CN 106355540A CN 201610830410 A CN201610830410 A CN 201610830410A CN 106355540 A CN106355540 A CN 106355540A
- Authority
- CN
- China
- Prior art keywords
- medium
- safety
- neutral net
- training
- gra
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000004458 analytical method Methods 0.000 title claims abstract description 16
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 41
- 238000012549 training Methods 0.000 claims abstract description 30
- 230000006870 function Effects 0.000 claims abstract description 14
- 238000012216 screening Methods 0.000 claims abstract description 9
- 238000007689 inspection Methods 0.000 claims abstract description 5
- 230000007935 neutral effect Effects 0.000 claims description 35
- 238000012360 testing method Methods 0.000 claims description 11
- 238000005457 optimization Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 5
- 238000013210 evaluation model Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 2
- 230000000052 comparative effect Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000010219 correlation analysis Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000003585 interneuronal effect Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 210000004218 nerve net Anatomy 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000001550 time effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000004836 empirical method Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000000505 pernicious effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network in order to overcome the defects of existing small- and medium-sized reservoir dam safety evaluating technology and method. The method comprises the steps of screening main factors influencing small- and medium-sized reservoir dam safety based on grey relational analysis method, and constructing a small- and medium-sized reservoir dam safety evaluation index system; generating a network training inspection sample; determining topological structure of the BP neural network; setting main training parameters of the BP neural network, initializing network connection weights and thresholds, and setting network training end conditions, error precision and training frequency; correcting the network by using L-M algorithm accumulative network global errors; entering the training and inspection samples to train and inspect the safety evaluating BP neural network, and finally constructing an intelligent dam safety evaluating model based on BP neural network. The method provides the functions such as comprehensive safety evaluation and hidden peril analysis, and is highly scientific, effective and practical.
Description
Technical field
The present invention relates to reservoir dam security study technical field, especially a kind of medium and small based on gra-bp neutral net
Type reservoir dam safe evaluation method, is widely used in safety analysis and the evaluation of all kinds of medium and small reservoirs dams.
Background technology
China's existing reservoir sum more than 90,000 8 thousand, is the most country of reservoir dam quantity in the world.Built is big
In dam, 96% is all middle-size and small-size dam, is that the flood control of the city of China and periphery, irrigation and generating electricity plays an important role.This
A little medium and small reservoirs dams are mostly earth and rockfill dam, run time more than 30～40 years, As time goes on, the growth in dam age,
The various conditions (as structure, basis, environment etc.) that dam runs gradually change, and build defect during dam in addition, run not
When factors such as, environmental changes, so that quite a few dam exists, design standard is low, foundation seepage, dam body materialses are aging
Rotten, the decay of dam structure the character even problem of the impact such as deterioration dam safety, in medium and small reservoirs dam, about 36% belongs to
Dangerously weak reseroirs dam, this present situation not only have impact on the performance of project benefit, also seriously threatens life and the wealth of the downstream people
The safety produced.The safety problem of reservoir dam, has been not only engineering safety, even more public safety problem.Therefore, how right
Scientific and reasonable evaluation is made in the safety of reservoir dam, for water power management work provide decision-making foundation, and as instruct and
Shi Jinhang reinforcement, it is ensured that reservoir dam safe operation, is related to the national economic development and people life property safety
Important topic, has important practical significance.
For the safety evaluation building up for many years and medium and small reservoirs dam of operation, the sights paying attention to maneuverability shapes abroad more
Examine, the analysis of observational data, domestic rely on existing Regulations, the quality paying attention to the construction time and initial operating stage to occur one
A little phenomenon analysis.For example: applied statistics Return Law analysis data of prototype observation, safe timeliness mathematical model is proposed；From reason of creeping
Derive the expression formula of dam body top time-effect displacement by setting out, with cyclic loadings such as periodic function simulated water pressures, and with non-linear
Square law carries out parameter estimation, and the same period also proposes with combination rheological model search time effect；Start with from physics analysis, propose
With dam safety state as system matter element, using matter-element theory, Region place value theory and its correlation function, setting up dam safety can
Open up comprehensive evaluation model；Judgment criteria is used as using the total degree of safety of dam (sd=ps/ η) and evaluates reservoir dam safe condition
Deng.Although the safety evaluation to dam works achieved with certain achievement at present, still there is problems in that (1) is to dam
The monitoring model of safety assessment and evaluation methodology are ripe not enough；(2) the current safety to concrete gravity dam, arch dam etc.
Evaluation study is more, but for middle-size and small-size dam such as earth and rockfill dam, due to the specific complexity of its engineering self-characteristic, dam material
Property, the difference of running status etc., lack targetedly appraisal model and method.Therefore, set up one kind and be directed to medium and small reservoirs
The evaluation methodology of dam general safety state and evaluation model are very necessary.
Content of the invention
Problem to be solved by this invention is for above-mentioned the deficiencies in the prior art, provides one kind to be based on gra-bp nerve net
The medium and small reservoirs system for evaluating safety method of network, it establishes the Safety Index System Assessment for medium and small reservoirs dam,
Construct the intelligent system for evaluating safety model based on bp neutral net, be capable of the functions such as safety evaluation, dangerous analysis, tool
There are higher science, effectiveness and practicality.
In order to solve above-mentioned technical problem, the present invention proposes technical scheme below: a kind of based on gra-bp neutral net
Medium and small reservoirs system for evaluating safety method is it is characterised in that it comprises the following steps:
Step 1: build medium and small reservoirs system for evaluating safety index system, screening impact medium and small reservoirs dam safety
Principal element, generates medium and small reservoirs system for evaluating safety index system；
Step 2: generate train samples；
Step 3: determine the topological structure of the bp neutral net for safety evaluation；
Step 4: training and inspection bp neutral net；
Step 5: the bp neural network model of performance appraisal that algorithm optimization is determined is used for the evaluation of new case.
Described step 1 concretely comprises the following steps: according to " system for evaluating safety directive/guide ", in conjunction with medium and small reservoirs dam safety characteristic
Primary election is carried out to evaluation index, calculates the correlation degree of each primary election index and evaluation result with gray relative analysis method, according to
Index grey relational grade result of calculation carries out index screening, sets up the Safety Index System Assessment for medium and small reservoirs dam.
Described step 2 is further: have collected a number of study and test samples, has carried out evaluation index original number
According to calculating and pretreatment.
Described step 3,4 it is further: set up gra (grey relational analysis)-ann (artificial
Neural networks) intelligent and safe evaluation model.
Described step 3 is further: design bp neural network topology structure: safety evaluation neutral net is that how defeated multi input is
The forward-type Three Tiered Network Architecture going out, including an input layer, a hidden layer and an output layer, input layer number is to comment
Valency index number, output layer nodes are 5, and rule of thumb formula and test determine node in hidden layer.
Described step 4 is further: determines bp neutral net relevant parameter: hidden layer transmission function adopts s type tangent letter
Number tansig, output layer transmission function adopts s type logarithmic function logsig；Set the main training parameter of bp neutral net, bag
Include learning rate and factor of momentum；Initial network connection weight and threshold value generate in interval [0,1] at random；Setting network is trained
End condition: error precision and frequency of training；Using bp neutral net described in l-m Algorithm for Training.
Described step 5 is further: improves optimization to bp neutral net；Adjust main training parameter, improve network
Training speed；Test samples in sample are substituted into safety evaluation neural network model test.
The method have the advantages that:
First, in terms of " system for evaluating safety directive/guide " 7 24 evaluation indexes, according to the Special safety of middle-size and small-size dam
Property, enter screening and the optimization of row index using grey correlation analysis, constitute middle-size and small-size system for evaluating safety index system, by subjectivity
Analysis judges to be converted into quantitative analytical calculation process, and specific aim and the science of index system have been effectively ensured；Secondly, collect
The authentic assessment case of a large amount of medium and small reservoirs dams, as neural metwork training test samples, has been effectively ensured bp neutral net
Training precision；Finally, system for evaluating safety and prediction are carried out using bp neural network algorithm, by known sample information
Study and training, find and grasp knowledge and the rule of expert opinion, traditional qualitative overall merit is converted into expert teacher, intelligence
The evaluation methodology of energy type.
Brief description
The invention will be further described with reference to the accompanying drawings and examples.
The flow chart of Fig. 1 present invention.
The topological structure schematic diagram of tri- layers of medium and small reservoirs system for evaluating safety bp neutral net of Fig. 2.
In Fig. 2: ωilI-th neuron representing input layer is to l-th interneuronal weights of hidden layer, ωljRepresent
L-th neuron of hidden layer is to j-th interneuronal connection weight of output layer.
Specific embodiment
Below in conjunction with the accompanying drawings embodiments of the present invention are described further.
For solving the problems, such as prior art, applicant has carried out deep, creative grinding to prior art
Study carefully work.The present invention proposes a kind of new medium and small reservoirs system for evaluating safety method based on gra-bp neutral net, leads to
Cross: 1) set up middle-size and small-size system for evaluating safety index system；2) analysis of safety monitoring data and process；3) using nerve
Network technology build system for evaluating safety model, can effectively solving the problems referred to above, realize the integrated dam safety of intelligent comprehensive
Evaluation methodology.
A kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net, comprises the steps:
Step 1: build medium and small reservoirs system for evaluating safety index system, screening impact medium and small reservoirs dam safety
Principal element, generates medium and small reservoirs system for evaluating safety index system；
Step 2: generate train samples；
Step 3: determine the topological structure of the bp neutral net for safety evaluation；
Step 4: training and inspection, optimization bp neutral net；
Step 5: the bp neural network model of performance appraisal that structure is completed is used for the evaluation of new case.
Specifically, the method to realize process as follows:
Step 1 is further: according to " system for evaluating safety directive/guide ", primary election from 24 two-level index of 7 first class index
21 factors mainly affecting middle-size and small-size dam safety, constitute preliminary Safety Index System Assessment；Index is to middle-size and small-size dam
There is no specific aim, enter screening and the optimization of row index using grey correlation analysis；According to index series and result Sequence composition
Curve between geometric similarity degree determining the degree of association between them, the geometry of curve is more similar, then this index with
The degree of association between evaluation result is bigger.Specifically comprise the following steps that
(1) selection of reference sequences and comparative sequences.It is reference sequences x that this method selects system for evaluating safety result0
K (), using the sequence of primary election index composition as comparative sequences.In this example, reference sequences are equal with the primary data of comparative sequences
For 5 dam (n1, n2, n3, n4, n5) sample datas, 21 Raw performance data of comparative sequences data table 1 represent, table 2
It is the evaluation result of the overall security Integrated Assessment On The Level of this 5 dams, be set to reference sequences.
Table 1 primary election index sample data table
Table 2 safety of dam overall merit grade
(2) data nondimensionalization.The initial data of each index is normalized, this method is returned using minimax
One change method.
(3) calculate correlation coefficient.Compare ordered series of numbers xiK () is to reference sequence x0K the coefficient of association of () is ξoiK (), utilizes
The coefficient of association computing formula of reference sequences index calculates:
The each sample sequence of table 3 is relative to the coefficient of association of reference sequence
(4) calculating correlation.It is γ that note compares ordered series of numbers and the degree of association of reference sequence, using the association of reference sequences index
Degree computing formula calculates:
The grey relational grade of each index of table 4
(5) the index degree of association compares.Quantitatively compare the quality of each evaluation index by the calculating of the degree of association, realize dam body
The screening of safety evaluation index and determination.The final deletion relatively low index of the degree of association, retains 15 indexs as evaluation index, builds
Vertical medium and small reservoirs dam assessment indicator system.
Above-mentioned steps 2 are further: collect a number of medium and small reservoirs dam safety overall merit example, by example
Index initial data carry out quantification treatment, then carry out related pretreatment, generate sample data.Wherein, a part of sample conduct
Neural network learning training sample, another part is as network checks sample.
The evaluation index initial data of table 5 dam sample
Step 3 is further: has carried out safety evaluation neural network structure and parameter designing.
The topological structure of the safety evaluation neutral net of present invention design is as follows: neutral net adopts multiple-input and multiple-output
Forward-type Three Tiered Network Architecture, mainly includes an input layer, a hidden layer and an output layer.
(1) input vector: filter out 15 evaluation indexes are set to the input layer of bp neural network model, that is, input
Node layer number is 15, and input vector is x=(x1,x2,...,x15)；
(2) output vector: using system for evaluating safety result grade as output vector, five kinds of safe conditions: normal, basic
Normally, respectively with 5 representation in components, output layer nodes are 5, output vector for mile abnormality, the abnormal and pernicious exception of severe
For y=(y1,y2,y3,y4,y5)；Evaluate state respectively with 5 representation in components for five kinds: (1,0,0,0,0), (0,1,0,0,0), (0,
0,1,0,0)、(0,0,0,1,0)、(0,0,0,0,1)；
(3) hidden layer: node in hidden layer is determined using empirical method additional examination survey method, final determination node in hidden layer is 10
Individual.
Neural network algorithm, parameter designing are as follows:
(1) transmission function
Hidden layer transmission function is set to s type tan tansig, functional form:Output layer transmits
Function is set to s type logarithmic function logsig, and its functional form is:
(2) error function
For each input sample p, standard error is: e=∑ ep=(∑ (dpj-opj)2)/2
Cumulative network global error:
In formula: opj、dpjRepresent the reality output to p-th input sample output unit j and desired output respectively.K represents
Arbitrary sample of m sample centering.
(3) modified weight improves and optimizates.Increase a momentum term in weighed value adjusting, weighed value adjusting vector expression is:
δ w (t)=η δ x+ α δ w (t-1)
In formula, w is certain layer of weight matrix, and x represents certain layer of input vector, and α is factor of momentum, typically has α ∈ (0,1).
Step 4 is further:
Training sample is divided into two parts, as network learning and training sample, another part is as network for a part
Test samples；
Set the main training parameter of bp neutral net, including learning rate and factor of momentum；
Initial network connection weight and threshold value generate in interval [0,1] at random；
The end condition of setting network training: error precision and frequency of training；
Using l-m algorithm, the network connection weights between each node layer and threshold value are adjusted, complete using cumulative network
Office's error carries out network correction, and input training and test samples are to the described medium and small reservoirs system for evaluating safety bp nerve net of training
Network.
Wherein, in literary composition, gra is: grey correlation analysis.Bp is: neutral net.
The preferred embodiment of the present invention described in detail above, but, the present invention is not limited in above-mentioned embodiment
Detail, in the range of the technology design of the present invention, multiple equivalents can be carried out to technical scheme, this
A little equivalents belong to protection scope of the present invention.
By above-mentioned description, those skilled in the art completely can be in the model without departing from this invention technological thought
In enclosing, carry out various change and modification all within protection scope of the present invention.The unaccomplished matter of the present invention, belongs to ability
The common knowledge of field technique personnel.
Claims (7)
1. a kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net it is characterised in that it include with
Lower step:
Step 1: build medium and small reservoirs system for evaluating safety index system, screening affects the main of medium and small reservoirs dam safety
Factor, generates medium and small reservoirs system for evaluating safety index system；
Step 2: generate train samples；
Step 3: determine the topological structure of the bp neutral net for safety evaluation；
Step 4: training and inspection bp neutral net；
Step 5: the bp neural network model of performance appraisal that algorithm optimization is determined is used for the evaluation of new case.
2. a kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net according to claim 1,
It is characterized in that, described step 1 concretely comprises the following steps: according to " system for evaluating safety directive/guide ", in conjunction with medium and small reservoirs dam safety
Characteristic carries out primary election to evaluation index, calculates the correlation degree of each primary election index and evaluation result with gray relative analysis method,
Index screening is carried out according to index grey relational grade result of calculation, sets up the safety evaluation index body for medium and small reservoirs dam
System.
3. a kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net according to claim 1,
It is characterized in that, described step 2 is further: have collected a number of study and test samples, has carried out evaluation index former
The calculating of beginning data and pretreatment.
4. a kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net according to claim 1,
It is characterized in that, described step 3,4 it is further: set up gra(grey relational analysis)-ann
(artificial neural networks) intelligent and safe evaluation model.
5. a kind of medium and small reservoirs system for evaluating safety side based on gra-bp neutral net according to claim 1 or 4
Method is it is characterised in that described step 3 is further: design bp neural network topology structure: how defeated safety evaluation neutral net be
Enter the forward-type Three Tiered Network Architecture of multi output, including an input layer, a hidden layer and an output layer, input layer
Number is evaluation index number, and output layer nodes are 5, and rule of thumb formula and test determine node in hidden layer.
6. a kind of medium and small reservoirs system for evaluating safety side based on gra-bp neutral net according to claim 1 or 4
Method is it is characterised in that described step 4 is further: determines bp neutral net relevant parameter: hidden layer transmission function adopts s type
Tan tansig, output layer transmission function adopts s type logarithmic function logsig；Set the main training ginseng of bp neutral net
Number, including learning rate and factor of momentum；Initial network connection weight and threshold value generate in interval [0,1] at random；Set net
The end condition of network training: error precision and frequency of training；Using bp neutral net described in l-m Algorithm for Training.
7. a kind of medium and small reservoirs system for evaluating safety method based on gra-bp neutral net according to claim 1,
It is characterized in that, described step 5 is further: improves optimization to bp neutral net；Adjust main training parameter, improve net
Network training speed；Test samples in sample are substituted into safety evaluation neural network model test.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610830410.8A CN106355540A (en) | 2016-09-18 | 2016-09-18 | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610830410.8A CN106355540A (en) | 2016-09-18 | 2016-09-18 | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106355540A true CN106355540A (en) | 2017-01-25 |
Family
ID=57859652
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610830410.8A Pending CN106355540A (en) | 2016-09-18 | 2016-09-18 | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106355540A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106769983A (en) * | 2016-12-23 | 2017-05-31 | 青海大学 | A kind of discrimination method of Cordyceps sinensis |
| CN106991531A (en) * | 2017-03-30 | 2017-07-28 | 国家电网公司 | Power distribution network project construction effect Post-assessment Method based on BP neural network |
| CN108710752A (en) * | 2018-05-17 | 2018-10-26 | 西南科技大学 | A kind of motor data analysis method based on grey correlation analysis and BP neural network |
| CN110263911A (en) * | 2019-04-30 | 2019-09-20 | 福建省水利投资开发集团有限公司 | A kind of rock-fill concrete technology fitness feedback regulation method neural network based |
| CN111563597A (en) * | 2020-05-11 | 2020-08-21 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for quickly estimating engineering quality of small reservoir and quickly diagnosing accidents |
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN112434750A (en) * | 2020-12-04 | 2021-03-02 | 中国电建集团华东勘测设计研究院有限公司 | Method for identifying dam monitoring data development pattern based on convolutional neural network |
| CN112488466A (en) * | 2020-11-13 | 2021-03-12 | 中国三峡建设管理有限公司 | High arch dam safety collaborative evaluation method and device |
| WO2021197009A1 (en) * | 2020-04-02 | 2021-10-07 | 中国长江三峡集团有限公司 | Real-time abnormal diagnosis and interpolation method for water regimen monitoring data |
| CN113554222A (en) * | 2021-07-19 | 2021-10-26 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation and control configuration method for bonding dam generalized bonding material |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101699477A (en) * | 2009-10-21 | 2010-04-28 | 东南大学 | Neural network method for accurately predicting dam deformation |
| CN103942922A (en) * | 2014-03-17 | 2014-07-23 | 湖南五舟检测科技有限公司 | Tailings safety monitoring method based on evolved neural network |
-
2016
- 2016-09-18 CN CN201610830410.8A patent/CN106355540A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101699477A (en) * | 2009-10-21 | 2010-04-28 | 东南大学 | Neural network method for accurately predicting dam deformation |
| CN103942922A (en) * | 2014-03-17 | 2014-07-23 | 湖南五舟检测科技有限公司 | Tailings safety monitoring method based on evolved neural network |
Non-Patent Citations (2)
| Title |
|---|
| 李浩平 等: "基于实例灰色关联分析的中小型水库大坝安全评价指标体系研究", 《科技创新与应用》 * |
| 缪新颖 等: "LM-BP神经网络在大坝变形预测中的应用", 《计算机工程与应用》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106769983A (en) * | 2016-12-23 | 2017-05-31 | 青海大学 | A kind of discrimination method of Cordyceps sinensis |
| CN106991531A (en) * | 2017-03-30 | 2017-07-28 | 国家电网公司 | Power distribution network project construction effect Post-assessment Method based on BP neural network |
| CN108710752A (en) * | 2018-05-17 | 2018-10-26 | 西南科技大学 | A kind of motor data analysis method based on grey correlation analysis and BP neural network |
| CN108710752B (en) * | 2018-05-17 | 2022-10-28 | 西南科技大学 | Motor data analysis method based on grey correlation analysis and BP neural network |
| CN110263911A (en) * | 2019-04-30 | 2019-09-20 | 福建省水利投资开发集团有限公司 | A kind of rock-fill concrete technology fitness feedback regulation method neural network based |
| CN111582634B (en) * | 2020-03-26 | 2024-02-23 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| WO2021197009A1 (en) * | 2020-04-02 | 2021-10-07 | 中国长江三峡集团有限公司 | Real-time abnormal diagnosis and interpolation method for water regimen monitoring data |
| GB2601261A (en) * | 2020-04-02 | 2022-05-25 | China Three Gorges Corp | Real-time abnormal diagnosis and interpolation method for water regimen monitoring data |
| JP2022541297A (en) * | 2020-04-02 | 2022-09-22 | 中国長江三峡集団有限公司 | Real-time anomaly diagnosis and interpolation method for water environment monitoring data |
| JP7333862B2 (en) | 2020-04-02 | 2023-08-25 | 中国長江三峡集団有限公司 | Real-time anomaly diagnosis and interpolation method for water environment monitoring data |
| GB2601261B (en) * | 2020-04-02 | 2024-05-15 | China Three Gorges Corp | Real-time abnormal diagnosis and interpolation method for water regimen monitoring data |
| CN111563597B (en) * | 2020-05-11 | 2023-07-04 | 水利部交通运输部国家能源局南京水利科学研究院 | Small reservoir engineering quality estimation and accident diagnosis rapid method |
| CN111563597A (en) * | 2020-05-11 | 2020-08-21 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for quickly estimating engineering quality of small reservoir and quickly diagnosing accidents |
| CN112488466A (en) * | 2020-11-13 | 2021-03-12 | 中国三峡建设管理有限公司 | High arch dam safety collaborative evaluation method and device |
| CN112434750A (en) * | 2020-12-04 | 2021-03-02 | 中国电建集团华东勘测设计研究院有限公司 | Method for identifying dam monitoring data development pattern based on convolutional neural network |
| CN112434750B (en) * | 2020-12-04 | 2023-05-16 | 中国电建集团华东勘测设计研究院有限公司 | Dam monitoring data development mode identification method based on convolutional neural network |
| CN113554222A (en) * | 2021-07-19 | 2021-10-26 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation and control configuration method for bonding dam generalized bonding material |
| CN113554222B (en) * | 2021-07-19 | 2023-11-28 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation configuration method for wide-source cementing material of cementing dam |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106355540A (en) | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network | |
| CN104018831B (en) | A kind of fractured well reservoir evaluation methods | |
| CN103226741B (en) | Public supply mains tube explosion prediction method | |
| CN105469196A (en) | Comprehensive evaluation method and comprehensive evaluation system for evaluating mine construction project process | |
| CN105930670A (en) | Model parameter uncertainty-based dynamic prediction method for river emergency pollution accident | |
| Zhang et al. | Evaluation and early warning of water environment carrying capacity in Liaoning province based on control unit: A case study in Zhaosutai river Tieling city control unit | |
| Ning et al. | GA-BP air quality evaluation method based on fuzzy theory. | |
| Song et al. | Study on turbidity prediction method of reservoirs based on long short term memory neural network | |
| CN113850516A (en) | Water quality evaluation method based on T-S fuzzy neural network | |
| Sanubari et al. | Flood modelling and prediction using artificial neural network | |
| Chen et al. | Characterizing the hydraulic connection of cascade reservoirs for short-term generation dispatching via Gaussian process regression | |
| Liu et al. | Improved design of risk assessment model for PPP project under the development of marine architecture | |
| Tu et al. | Evaluation of seawater quality in hangzhou bay based on TS fuzzy neural network | |
| Lyu et al. | Water level prediction model based on GCN and LSTM | |
| CN114065624A (en) | Intelligent evaluation method for pipe network integrity management level of refining device | |
| CN103942600A (en) | Sludge bulking prediction method based on peak self-organizing radial basis function neural network | |
| CN111797577A (en) | Method and system for evaluating adaptability of typical remediation engineering of estuary and river network | |
| Wen | Construction project risk evaluation based on rough sets and artificial neural networks | |
| Feder | Machine-Learning-Based Early-Warning System Maintains Stable Production | |
| Chen et al. | A comparative study on multi-regression analysis and BP neural network of PM2. 5 index | |
| Solgi et al. | Hybrid Models Performance Assessment to Predict Flow of Gamasyab River | |
| CN105654395A (en) | Comprehensive water quality evaluating method based on compensation type Hopfield neural network | |
| Chen et al. | Application of BP Neural Network Based on the Genetic Algorithm in Secondary Modeling of Air Quality Forecast [J] | |
| Kim et al. | Weight evaluation of factors influencing road flood risk in Mekong river delta | |
| Jiang et al. | A prediction model of shallow groundwater pollution based on deep convolution neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170125 |