CN104636508A - Positioning method based on particle swarm optimization algorithm and centroiding algorithm - Google Patents
Positioning method based on particle swarm optimization algorithm and centroiding algorithm Download PDFInfo
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- CN104636508A CN104636508A CN201310548192.5A CN201310548192A CN104636508A CN 104636508 A CN104636508 A CN 104636508A CN 201310548192 A CN201310548192 A CN 201310548192A CN 104636508 A CN104636508 A CN 104636508A
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Abstract
The invention discloses a positioning method based on a particle swarm optimization algorithm and a centroiding algorithm. The positioning method comprises the following concrete steps that finite element analysis is carried out on a slope, physical parameters are collected, and a three-dimensional finite element model of the slope is obtained; a computer is used for computing the finite element model to obtain the position of the gravity center of the slope; finite element analysis is carried out on the slope to be detected, slope data, related to the current slope parameters, in a simulation database in a database are called, a forecasting function is trained through an artificial neural network, and a mature target function is obtained; the finite elements and the physical parameters of the slope to be detected are substituted in the mature target function, and a forecasting result of the current sliding displacement sequence is obtained. Compared with a traditional analysis method, the positioning method has higher and more scientific analysis precision and can forecast the slope movement more conveniently.
Description
Technical field
The present invention relates to a kind of localization method based on particle swarm optimization algorithm and centroid algorithm.
Background technology
Slope deforming is by the various factors such as precipitation, underground water table, internal stress change, and cause the deformation mechanism of different side slope to be not quite similar, its deformation process has complicacy, randomness and uncertainty, and the Deformation Prediction of side slope remains a difficult problem.
Side slope is due to contour structures complexity, usually there is irregular profile and profile, when mechanical analysis, be usually difficult to the center of gravity that can calculate edge slope structure, with regard to being difficult to, the prediction of corresponding globality is done, for road safety brings great hidden danger to the movement tendency of side slope.
The kind of present side slope is relatively fixing, and namely physical property such as density is relative fixing with information such as materials, and the development of measuring technique, also can know the 3D shape of side slope accurately.
Summary of the invention
The present invention is directed to the proposition of above problem, and a kind of localization method based on particle swarm optimization algorithm and centroid algorithm of development, there are following steps:
-finite element analysis is carried out to side slope and gathers physical parameter, obtain the three-dimensional finite element model of side slope; Use computing machine to be described finite element model, calculate the position of side slope center of gravity;
-repeat above-mentioned steps, set up side slope center of gravity database;
-finite element analysis is carried out to side slope to be detected, according to the quantity of finite element and the physical parameter of side slope, set up the anticipation function of the center of gravity of current side slope;
Slope data relevant to current slope parameter in simulated database in-calling data storehouse, the anticipation function described in the training of end user's artificial neural networks, obtains ripe objective function;
-finite element of described side slope to be detected and physical parameter are brought into the objective function of described maturation, obtain predicting the outcome of current slide displacement sequence.
The current side slope center of gravity calculated by anticipation function is predicted the outcome, adopts centroid algorithm to carry out modified result.
In described database, slope data at least comprises: side slope kind, side slope size, edge slope structure and side slope material.
For the finite element unique point of side slope to be measured, use the method for interpolation, be similar to the finite element of approximate side slope in database.
According to analyzing the finite element drawing side slope to be detected, transferring slope data similar with it in a database, forming similar data set, use the objective function described in set of metadata of similar data set pair to train.
Owing to have employed technique scheme, a kind of localization method based on particle swarm optimization algorithm and centroid algorithm provided by the invention, by carrying out a large amount of analytical tests to existing side slope, forming database, finite element analysis is carried out to side slope to be detected, form target analysis function, function end user artificial neural networks is trained, finally obtains the centre of gravity place of side slope, compared to traditional analytical approach, there is higher more scientific analysis precision, very convenient prediction side slope motion.
Accompanying drawing explanation
In order to the technical scheme of clearer explanation embodiments of the invention or prior art, introduce doing one to the accompanying drawing used required in embodiment or description of the prior art simply below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is process flow diagram of the present invention
Embodiment
For making the object of embodiments of the invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, clear complete description is carried out to the technical scheme in the embodiment of the present invention:
As shown in Figure 1: a kind of localization method based on particle swarm optimization algorithm and centroid algorithm, mainly comprises the steps:
First, finite element analysis carried out to side slope and gathers physical parameter, obtaining the three-dimensional finite element model of side slope; Use computing machine to be described finite element model, calculate the position of side slope center of gravity.
Then, repeat above-mentioned steps, set up side slope center of gravity database.
Secondly, finite element analysis is carried out to side slope to be detected, according to the quantity of finite element and the physical parameter of side slope, sets up the anticipation function of the center of gravity of current side slope;
Slope data relevant to current slope parameter in simulated database in calling data storehouse, the anticipation function described in the training of end user's artificial neural networks, obtains ripe objective function.
Finally, the finite element of described side slope to be detected and physical parameter are brought into the objective function of described maturation, obtain predicting the outcome of current slide displacement sequence.
In order to further increase the precision of measuring and calculating, as a preferably embodiment, the current side slope center of gravity calculated by anticipation function being predicted the outcome, adopting centroid algorithm to carry out modified result.
In order to increase the precision of prediction, ensureing when having some samples, ensureing precision of prediction.As a preferably embodiment, in described database, slope data at least comprises: side slope kind, side slope size, edge slope structure and side slope material.
Consider, the finite element of side slope in real process, may not be corresponding with the finite element in database, for the finite element unique point of side slope to be measured, uses the method for interpolation, is similar to the finite element of approximate side slope in database.
In order to reduce operand, as a preferably embodiment, according to analyzing the finite element drawing side slope to be detected, transferring slope data similar with it in a database, forming similar data set, use the objective function described in set of metadata of similar data set pair to train.
The above; be only the present invention's preferably embodiment; but protection scope of the present invention is not limited thereto; anyly be familiar with those skilled in the art in the technical scope that the present invention discloses; be equal to according to technical scheme of the present invention and inventive concept thereof and replace or change, all should be encompassed within protection scope of the present invention.
Claims (5)
1., based on a localization method for particle swarm optimization algorithm and centroid algorithm, there are following steps:
-finite element analysis is carried out to side slope and gathers physical parameter, obtain the three-dimensional finite element model of side slope; Use computing machine to be described finite element model, calculate the position of side slope center of gravity;
-repeat above-mentioned steps, set up side slope center of gravity database;
-finite element analysis is carried out to side slope to be detected, according to the quantity of finite element and the physical parameter of side slope, set up the anticipation function of the center of gravity of current side slope;
Slope data relevant to current slope parameter in simulated database in-calling data storehouse, the anticipation function described in the training of end user's artificial neural networks, obtains ripe objective function;
-finite element of described side slope to be detected and physical parameter are brought into the objective function of described maturation, obtain predicting the outcome of current side slope center of gravity.
2. a kind of localization method based on particle swarm optimization algorithm and centroid algorithm according to claim 1, is further characterized in that: predict the outcome for the current side slope center of gravity calculated by anticipation function, adopts centroid algorithm to carry out modified result.
3. a kind of localization method based on particle swarm optimization algorithm and centroid algorithm according to claim 1, is further characterized in that: in described database, slope data at least comprises: side slope kind, side slope size, edge slope structure and side slope material.
4. a kind of localization method based on particle swarm optimization algorithm and centroid algorithm according to claim 1, is further characterized in that: for the finite element unique point of side slope to be measured, uses the method for interpolation, is similar to the finite element of approximate side slope in database.
5. a kind of localization method based on particle swarm optimization algorithm and centroid algorithm according to claim 3, be further characterized in that: according to analyzing the finite element drawing side slope to be detected, transfer slope data similar with it in a database, form similar data set, use the objective function described in set of metadata of similar data set pair to train.
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| CN201310548192.5A CN104636508A (en) | 2013-11-07 | 2013-11-07 | Positioning method based on particle swarm optimization algorithm and centroiding algorithm |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107113764A (en) * | 2015-05-22 | 2017-08-29 | 华为技术有限公司 | The method and apparatus for improving artificial neural network positioning performance |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107113764A (en) * | 2015-05-22 | 2017-08-29 | 华为技术有限公司 | The method and apparatus for improving artificial neural network positioning performance |
| CN107113764B (en) * | 2015-05-22 | 2020-02-14 | 华为技术有限公司 | Method and device for improving positioning performance of artificial neural network |
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Application publication date: 20150520 |
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