CN106910026A - Method of the Perioperative cardiac events to fisheries impact is evaluated based on fuzzy neural network - Google Patents
Method of the Perioperative cardiac events to fisheries impact is evaluated based on fuzzy neural network Download PDFInfo
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Abstract
Method of the Perioperative cardiac events to fisheries impact is evaluated based on fuzzy neural network, the invention belongs to field of environment protection, its method is to extract the key element that Perioperative cardiac events engineering produces influence on fishery first, builds Perioperative cardiac events to fishery elements affect degree evaluation index system；Then all kinds of influent factors are built with fuzzy neural network subnet study weight and Evaluations matrix, first order fuzzy overall evaluation is completed；It is finally based on subnet evaluation result and builds fuzzy neural network major network, completes second level fuzzy overall evaluation, influence degree of the assessment Perioperative cardiac events engineering to fishery.The fuzzy neural network that the present invention is used is the combination of fuzzy system and neutral net, and it has drawn both strong points, can the single neutral net of ratio of components or the single more preferable system of fuzzy system performance.
Description
Technical field
A kind of appraisal procedure that the present invention influences for Perioperative cardiac events engineering on fishery resources, it is proposed that multistage fuzzy neural
Assessing network algorithm.
Background technology
In recent years, as developing rapidly for China's Coastal Areas economy rapidly increases with population, land resource is more come rare.
To alleviate land supply and need contradiction, survival and development space is expanded in coastal area by Perioperative cardiac events activity, and Perioperative cardiac events are also region society
Meeting, expanding economy provide important space guarantee.
Perioperative cardiac events also limit the development space of sea fishery while huge social and economic benefit is brought, infringement
Ecological benefits.Perioperative cardiac events activity covers biological habitat by way of landfill, causes many lifes such as bio-diversity reduction
State problem, very big impact is brought to the sustainable development of periphery marine fishery resources.Additionally, what is produced in work progress is big
Amount suspension bed sediment, causes water body muddiness, dissolved oxygen concentration reduction, directly the breathing to the biological especially biological young and Digestive
System is impacted.Perioperative cardiac events can also change ocean hydrodynamic condition, cause tidal current dynamics to weaken, and current take husky ability reduction, draw
Play water body and substrate physicochemical property changes, influence biological physiology and history of life process.Thus, the excessive exploitation of Perioperative cardiac events is bound to shadow
Ring the sustainable development of sea fishery and ecological environment.
Impact evaluation of the Perioperative cardiac events development activities to fishery resources is the crucial ring of construction project marine environmental impact assessment
Section, how quickly, quantitative assessment Perioperative cardiac events development activities the influence degree of fishery resources is had become marine environment influence comment
One technical barrier in valency field.At present, from the point of view of domestic and international pertinent literature, many scholars use fuzzy mathematics, neutral net
Method, analytic hierarchy process (AHP), gray theory, Bayesian network, rough set theory etc. set up evaluation model, obtain many achievements in research.
Fuzzy overall evaluation and BP neural network method, are to use relatively broad method at present.The advantage of fuzzy overall evaluation is energy
Ambiguity and uncertain problem are solved well, it is simple and easy to apply, but fuzzy composition computing existence information is lost, fuzzy set quantifies
With subjective random problem, the irrational phenomenon of unclear classification, result also occurs sometimes.The advantage of neutral net is tool
There are adaptive ability and fault tolerant, non-linear, non-locality problem can be processed, evaluation result is objective.Have the disadvantage to be only suitable for
In some quantitative datas are processed, lack analysis, disposal ability to qualitative index.This invention combine fuzzy overall evaluation and
The advantage of neutral net, has evaded two kinds of deficiencies of evaluation method so that evaluation result is more objective, accurate, reasonable.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of Perioperative cardiac events engineering based on fuzzy neural network to fishery
Real estate impact evaluation method, the method that the present invention is combined using fuzzy overall evaluation and neutral net, both take into account fuzzy
Property, also utilizing neutral net has the advantages that preferable learning ability, accelerates the study adjustment of weight.
The present invention is achieved by the following technical solution：
A kind of Perioperative cardiac events engineering based on fuzzy neural network influences evaluation method to fishery resources, and it includes following step
Suddenly：
1) analysis Perioperative cardiac events engineering produces influence to fishery, extracts impacted staple, builds Perioperative cardiac events to fishery
Influence degree assessment indicator system；
2) fuzzy neural network subnet study weight and Evaluations matrix are built, first order fuzzy overall evaluation, assessment is completed
Perioperative cardiac events engineering is to all kinds of fishery elements affect degree；
3) build fuzzy neural network major network and complete second level fuzzy overall evaluation, assessment Perioperative cardiac events engineering is to fisheries impact
Degree；
4) relatively the fuzzy-neural network method for using of the invention with fuzzy overall evaluation and neutral net is used alone
Evaluation result.
Further, in above-mentioned steps 1) in staple refer to quality of water environment, depositional environment and fish production three
Key element.
Further, in above-mentioned steps 2) in fuzzy neural network subnet be that a mould is set up to each class fishery resources key element
Paste neural network model；Weight and Judgement Matrix in fuzzy overall evaluation as neutral net weight, to fuzznet
Network is trained, and the Evaluations matrix as neutral net weight is modified further according to error-duration model method, finally according to amendment
Afterwards plus Evaluations matrix, adjust weight according to maximum membership degree weighted average deviation method.
Further, in above-mentioned steps 3) in fuzzy neural network major network be that fishery resources are set up with a fuzzy neural network
Model, the Evaluations matrix that the evaluation result matrix obtained with subnet learning success learns as major network, the evaluation result for obtaining is
Evaluation of the Perioperative cardiac events engineering to fishery resources influence degree.
Present invention beneficial effect compared with prior art：
Multistage fuzzy neural network evaluation algorithms proposed by the present invention, both take into account Fuzzy Assessment Analysis not true
Qualitative question, also utilizing neutral net has the advantages that preferable learning ability, accelerates the study adjustment of weight.Empirical tests
(seeing below the specific implementation of literary step 4), effect is substantially better than exclusive use fuzzy overall evaluation and neutral net assessment Perioperative cardiac events
Engineering influences on fishery resources.
Brief description of the drawings
The flow chart of the appraisal procedure that Fig. 1 Perioperative cardiac events engineering influences on fishery resources；
The neural network structure of Fig. 2 fuzzy neural network subnet models；
The neural network structure of Fig. 3 fuzzy neural network major network models；
The comparing of Fig. 4 fuzzy neural networks and fuzzy overall evaluation and neutral net；
The comparing of fuzzy neural network and fuzzy overall evaluation and neutral net after Fig. 5 sequences.
Specific embodiment
Technical scheme is described in detail with reference to the accompanying drawings and examples.
Embodiment
Fig. 1 is the flow chart of the appraisal procedure that Perioperative cardiac events engineering influences on fishery resources
Step 1：Analysis Perioperative cardiac events engineering produces influence to fishery resources, extracts impacted staple, and structure encloses to be filled out
Sea is to fishery resources influence degree assessment indicator system.
Step 2：Fuzzy neural network subnet study weight and Evaluations matrix are built, first order fuzzy overall evaluation is completed,
Assessment Perioperative cardiac events engineering is to all kinds of fishery resources elements affect degree.
Step 3：Build fuzzy neural network major network and complete second level fuzzy overall evaluation, assessment Perioperative cardiac events engineering is to fishery
Element of resource influence degree.
Step 4：Compare the fuzzy-neural network method for using of the invention and fuzzy overall evaluation and nerve net is used alone
The evaluation result of network.
Described step 1 is described as follows：
Perioperative cardiac events engineering is mainly to fisheries environment and Fishery value Influence of production to fisheries impact, therefore water environment, heavy
Product environment and the important impacted key element of fish production 3 are the keys for analyzing Perioperative cardiac events engineering to fisheries impact.
Perioperative cardiac events can produce a large amount of suspension bed sediments in work progress, cause water body muddiness, dissolved oxygen concentration reduction, directly
Biological breathing and digestive system are impacted, the particularly biological young is difficult to escape.Suspended particulate makes under seawater transparency
Drop, is unfavorable for the photosynthesis of phytoplankton, and phytoplankton is reduced, and influences whole food chain.Perioperative cardiac events can also change ocean water
Dynamic condition, causes tidal current dynamics to weaken, and current take husky ability reduction, cause water temperature and Salinity change, and biological needs are one
The temperature and salinity for determining scope could survive.
Perioperative cardiac events are engaged in life production activity and deposit certainly will be affected, and mainly heavy metal pollution is raw
Neo-Confucianism shows that heavy metal Hg, cadmium, lead, copper and arsenic are larger to the toxicity of marine organisms.
Perioperative cardiac events damage biological habitat, reduce the area for being available for fishing for and cultivate, and Perioperative cardiac events are developed newly in addition
Industry, cause fish production to fail, marine capture production, sea water breeding area, fish-egg larva and juvenile quantity and fishery resources into
Body quantity is reduced.
Therefore, it can build Perioperative cardiac events to fisheries impact degree evaluation index system, as shown in table 1：
The Perioperative cardiac events of table 1 are to fisheries impact degree evaluation index system
Described step 2 is described as follows：
Fuzzy neural network subnet is to set up a fuzzy neural network model to each class fisheries impact key element.Evaluation is enclosed
Sea is filled out to fishery uiThe neural network structure of influence degree model as shown in Fig. 2 it is five layers of feedforward neural network, by one
Individual input layer, an output layer and three hidden layers are constituted.
Structure of fuzzy neural network figure implication is as follows：
Ground floor：Input layer, fisheries impact key element ui, the corresponding two-level index x of i=1,2,3ij, j=1,2 ..., ni,ni
It is fisheries impact key element uiCorresponding index number.
The second layer：Weight in fuzzy overall evaluation, is calculated by maximum membership degree weighted average deviation method and tried to achieve.
Third layer：Evaluations matrix in fuzzy overall evaluation, rijhIt is index xijThe fuzzy relation having with h-th comment
Degree, comment is respectively { serious, more serious, typically, more slightly, slight }, and value is respectively [0.8,1], [0.6,0.8),
[0.3,0.6), [0.1,0.3), [0,0.1).
4th layer：It is maximizing operation.
Layer 5：It is output layer, the evaluation result b of outputih。
The specific procedure algorithm of adjustment weight and Judgement Matrix is as follows：
1. fisheries impact key element u is giveniInitial evaluation matrixDesired output vector
Assigned error threshold epsiloni, noteIt is the Evaluations matrix in t periods：
2. maximum membership degree is calculatedWherein
3. the deviation of Judgement Matrix is calculated, is designated as
4. weight vectors are calculatedAccording to maximum membership degree weighted average deviation
Fa Ke get：
5. by model of fuzzy synthetic evaluationCalculate the output of real network；
6. relative error is calculatedIfNext step is then transferred to,
Otherwise return to the 2nd step；
7. Evaluations matrix, note are correctedObtain the Evaluations matrix in t+1 periodsWherein
8. the 2nd step to the 5th step is repeated, if meetingNext step is then transferred to, the 2nd step is otherwise returned to；
9. compareWithTo the ε for givingiIf,ThenI.e.
It is required Evaluations matrix, stops iteration, otherwise returns to the 2nd step.
Described step 3 is described as follows：
The neural network structure of fuzzy neural network major network model is as shown in figure 3, it is also five layers of Feedforward Neural Networks
Network.
Structure of fuzzy neural network figure implication is as follows：
Ground floor：Input layer, fisheries impact key element ui, i=1,2,3.
The second layer：3 are judged the first order judge that subnet constitutes completion system, and its output result constitutes second level judge
Evaluations matrix.
Third layer：Ranking operation, the output of the second layer and corresponding weights (W1,W2,W3) do product calculation.
4th layer：It is summation operation.
Layer 5：It is output layer, obtains final evaluation result bh, h=1,2 ..., 5.
The algorithm steps of major network are similar to subnet, and specific procedure is as follows：
1. initial network weights A(0), desired output vectorAssigned error threshold epsilon；
2. input pointer value and desired output, obtain the output B of real network(t)；
3. relative error is calculatedIfThen network training knot
Beam, is otherwise transferred to next step；
4. corrective networks weights, rememberObtain the network weight in t+1 periodsWherein
5. the 2nd step to the 3rd step is repeated, if meetingNext step is then transferred to, the 1st step is otherwise returned to.
The differentiation result of result output is exactly the influence degree of the Perioperative cardiac events to fishery of present invention assessment.
Described step 4 is described as follows：
Fuzzy overall evaluation is used alone and neural network model evaluates influence degree of the Perioperative cardiac events to fishery key element, with this
The fuzzy neural network algorithm of invention compares, and comparative result is as shown in table 2 and Fig. 4 and Fig. 5：
The comparing of the fuzzy neural network of table 2 and fuzzy overall evaluation and neutral net
From table 2 and Fig. 4,5 as can be seen that fuzzy neural network broken line fluctuates smaller, and widen than shallower after sequence
The distance of each Perioperative cardiac events project evaluation result, reduces the ambiguity of result.As can be seen here, fuzzy neural network is solving to comment
Sentence problem aspect, not only overcome the precision problem in fuzzy system, and effect is used alone artificial neural network than general
Effect it is good.
In summary analyze, the side that the present invention of the present invention is combined using fuzzy overall evaluation and neutral net
Method, both take into account Fuzzy Assessment Analysis uncertain problem, also utilize neutral net have preferable learning ability,
Accelerate the advantage of the study adjustment of weight.Empirical tests, effect is substantially better than exclusive use fuzzy overall evaluation and neutral net is commented
Estimate influence of the Perioperative cardiac events engineering to fishery.
Claims (4)
1. a kind of Perioperative cardiac events engineering based on fuzzy neural network influences evaluation method to fishery resources, it is characterised in that it includes
Following steps：
1) analysis Perioperative cardiac events engineering produces influence to fishery, extracts impacted staple, builds Perioperative cardiac events to fisheries impact
Degree evaluation index system；
2) fuzzy neural network subnet study weight and Evaluations matrix are built, first order fuzzy overall evaluation is completed, assessment is enclosed and filled out
Extra large engineering is to all kinds of fishery elements affect degree；
3) build fuzzy neural network major network and complete second level fuzzy overall evaluation, assessment Perioperative cardiac events engineering is to fisheries impact journey
Degree.
2. a kind of Perioperative cardiac events engineering based on fuzzy neural network according to claim 1 influences evaluation side to fishery resources
Method, it is characterised in that the step 1) in staple refer to quality of water environment, three key elements of depositional environment and fish production.
3. a kind of Perioperative cardiac events engineering based on fuzzy neural network according to claim 1 influences evaluation side to fishery resources
Method, it is characterised in that the step 2) in fuzzy neural network subnet be that a fuzzy god is set up to each class fishery resources key element
Through network model；Weight and Judgement Matrix in fuzzy overall evaluation are entered as the weight of neutral net to fuzzy neural network
Row training, is modified, finally according to revised further according to error-duration model method to the Evaluations matrix as neutral net weight
Plus Evaluations matrix, adjust weight according to maximum membership degree weighted average deviation method.
4. a kind of Perioperative cardiac events engineering based on fuzzy neural network according to claim 1 influences evaluation side to fishery resources
Method, it is characterised in that the step 3) in fuzzy neural network major network be that fishery resources are set up with a fuzzy neural network mould
Type, the Evaluations matrix that the evaluation result matrix obtained with subnet learning success learns as major network, the evaluation result for obtaining is to enclose
Evaluation of the reclamation to fishery resources influence degree.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101944160A (en) * | 2010-08-31 | 2011-01-12 | 环境保护部华南环境科学研究所 | Immediate offshore area ecological environment comprehensive evaluation method based on analytic hierarchy process and comprehensive evaluation method |
| CN106778013A (en) * | 2016-12-29 | 2017-05-31 | 钦州学院 | A kind of integrated evaluating method of offshore sea waters ecological environment |
-
2017
- 2017-03-03 CN CN201710126631.1A patent/CN106910026A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101944160A (en) * | 2010-08-31 | 2011-01-12 | 环境保护部华南环境科学研究所 | Immediate offshore area ecological environment comprehensive evaluation method based on analytic hierarchy process and comprehensive evaluation method |
| CN106778013A (en) * | 2016-12-29 | 2017-05-31 | 钦州学院 | A kind of integrated evaluating method of offshore sea waters ecological environment |
Non-Patent Citations (5)
| Title |
|---|
| 刘增良主编: "《模糊技术与神经网络技术选编4》", 31 October 1999 * |
| 朱永贵: "集约用海对海洋生态影响的评价研究--以莱州湾为例", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 * |
| 谈自忠主编: "《第二届全球华人智能控制与智能自动化大会论文集下》", 31 December 1997 * |
| 贺义雄 等: "溢油对海洋渔业产业中长期影响评价指标体系研究", 《中国渔业经济》 * |
| 陈虎: "绿色铁路客站节地与室外环境评价方法的研究", 《铁道科学与工程学报》 * |
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