CN110288606A - A kind of three-dimensional grid model dividing method of the extreme learning machine based on ant lion optimization - Google Patents
A kind of three-dimensional grid model dividing method of the extreme learning machine based on ant lion optimization Download PDFInfo
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Abstract
The invention discloses the three-dimensional grid model dividing methods of the extreme learning machine optimized based on ant lion, compared with prior art, the present invention is achieved the object of the present invention using step 1 to step 7, the advantage is that: using ant in ant lion optimization algorithm by the double influence of elite ant lion and roulette strategy, iteration updates and optimal solution is assigned to elite ant lion, to improve and optimizate the input weight and hidden layer bias matrix that extreme learning machine generates at random, training obtains a high-precision segmentation and classification device and tests Princeton model.Compared to the work of forefathers, on 6 class model data set of Airplane, Ant, Chair, Octopus, Teddy and Fish, model algorithm time-consuming 1000s or so of the training dough sheet number between 20k-30k, and the dividing method than being not optimised obtains the higher accuracy of separation, the highest accuracy of separation reaches 99.49%.
Description
Technical field
The invention belongs to 3 d image data processing technology fields, and in particular to a kind of limit study based on ant lion optimization
The three-dimensional grid model dividing method of machine, this method are used to carry out semantic segmentation to existing three-dimensional grid model.
Background technique
Have benefited from the rapid development of hardware sensor and reconstruction technique, the quantity of miscellaneous digital multimedia data is not
It is disconnected to increase.It is analyzed and is handled for 3 d image data, become the hot spot of computer graphics study.Wherein grid model
Segmentation be research topic that Fundamentals of Computer Graphics is also importance.The segmentation of grid model is the geometry according to model
Or topological characteristic, it is decomposed, becomes certain amount and communicate with each other, each part has simple shape meaning sub-grid
The work of piece.Researchers at home and abroad propose many methods for grid model segmentation.According to whether the label of model is made
To input information, the segmentation of grid model can be divided into: segmentation and unsupervised segmentation two major classes based on supervision.Due to based on prison
The very few manual intervention of the dividing method superintended and directed, shows higher segmentation precision.In recent years, researcher proposed in succession
Dividing method based on supervision.But disappear currently based on the grid model dividing method of the deep learning training segmentation and classification device time
Consumption is especially big, how effectively to reduce the training time, and further increases the accuracy of grid model segmentation, is to need at present
The problem of focusing on solving.For some emerging Swarm Intelligent Algorithms, not only simple and flexible but also part can be effectively avoided
It is optimal, so that it is used widely in solving optimization problem.How by colony intelligence optimization theory be applied to threedimensional model
It is the hot issue for needing further to research and solve in dividing processing.
Summary of the invention
It is king-sized in order to solve the grid model dividing method training segmentation and classification device time loss based on deep learning
Disadvantage, and the accuracy of grid model segmentation is further increased, this paper presents a kind of limit study based on ant lion optimization
The three-dimensional grid model dividing method of machine.
A kind of three-dimensional grid model dividing method of the extreme learning machine based on ant lion optimization, it is described to be optimized based on ant lion
The three-dimensional grid model dividing method of extreme learning machine comprise the steps of:
Step 1, for Princeton model data concentrate Airplane, Ant, Chair, Octopus, Teddy,
6 class three-dimensional grid model of Fish carries out dough sheet feature extraction to each three-dimensional grid model, and is normalized, normalizing
Change between [- 1,1], eliminate dimension impact, wherein dough sheet feature, comprising: dough sheet curvature feature, PCA feature, in shape under
Literary feature, average geodesic distance feature, shape characteristics of diameters.
Step 2, Airplane, Ant, Chair, Octopus, Teddy, Fish Princeton model data concentrated
6 class three-dimensional grid models, are divided into training dataset and test data set in proportion respectively, due in the data set of Princeton
6 class three-dimensional grid model of Airplane, Ant, Chair, Octopus, Teddy, Fish, the quantity of every class grid model are all
20, random selection 95% namely 19 models are as training dataset, for training segmentation and classification device, remaining 5%,
That is 1 model is as test data set, for testing to trained segmentation and classification device.
Step 3, the input feature value that obtained every class three-dimensional grid model training dataset is divided based on step 2 is established
P, specifically: every class three-dimensional grid model training data is concentrated to the model for including, in step 1 the dough sheet after normalized
Feature merges, and forms input feature value P, is expressed as P={ (x for the input feature value P that sample number is Mj,tj),j
=1,2 ..., M }, wherein xj=(xj1,xj2,…,xjn)T∈Rn, indicate to be directed to the extracted dough sheet feature of three-dimensional grid model, n
For the attribute size of input feature value P, tj=(tj1,tj2,…,tjm)T∈RmOr it is expressed as matrixIt represents
The label that three-dimensional grid model is beaten.
Step 4, the testing feature vector that obtained every class three-dimensional grid model test data set is divided based on step 2 is established
P', specifically: the dough sheet feature after the three-dimensional grid model of test data concentration in step 1 normalized is formed into test
Feature vector P'.
Step 5, the size of the hidden neuron L parameter of extreme learning machine model is set, specifically: by hidden neuron
Number L is set as 300, and basic extreme learning machine algorithm is realized by step 5.1 to step 5.3:
Step 5.1, random to generate input weight wiB is biased with hidden layeri；
Step 5.2, by formulaHidden layer output matrix H is calculated, wherein
G () indicates activation primitive；
Step 5.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix, matrixRepresent the label that three-dimensional grid model is beaten.
Step 6, extreme learning machine is optimized using ant lion optimization algorithm, maximum number of iterations in ant lion optimization algorithm is set
ImaxIt is 20 for 25 and population scale N, using the input feature value P obtained based on step 3 as input data, is input to ant lion
It is trained in the extreme learning machine model of optimization, training obtains optimal segmentation and classification device, and the survey to obtaining based on step 4
Examination feature vector P' is split the specific of test and executes operation, is realized by step 6.1 to step 6.6:
Step 6.1, random initializtion ant and ant lion population, according to formulaIn limitation
The size of condition, random initializtion ant and ant lion population is N × L × (n+1), each ant and ant lion individual in population
Position indicates extreme learning machine parameter combination f (w to be optimizedi,bi)；
Step 6.2, the fitness value of every ant lion is calculated, and optimal is considered as elite ant lion, extremely by step 6.2.1
Step 6.2.7 is realized:
Each ant lion individual in ant lion population is divided into the input weight w that size is L × n by step 6.2.1i, size
It is the hidden layer biasing b of L × 1i；
Step 6.2.2, by formulaHidden layer output matrix H is calculated,
Middle g () indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.2.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix, matrixRepresent the label that three-dimensional grid model is beaten；
Step 6.2.4, the segmentation and classification device obtained by input feature value P training, wherein segmentation and classification device includes input
Weight, hidden layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.2.5；
Step 6.2.6, calculates the fitness value of ant lion, i.e. the segmentation of testing feature vector P' three-dimensional grid model is accurate
Degree, the accuracy of separation are the ratio between the grid surface number being correctly marked in grid model and total grid surface number；
Step 6.2.7 compares the size of the fitness value of each individual in ant lion population, from big to small according to fitness value
Sequence, is assigned to elite ant lion for optimal；
Step 6.3, t=1 is enabled, into iterative cycles, updates the position f (w of anti,bi), by step 6.3.1 to step
6.3.3 it realizes:
Step 6.3.1 calculates influence of the roulette strategy to ant random walk, obtains roulette strategy and changes at the t times
For when the random walk that selects
Step 6.3.2 calculates influence of the elite ant lion to ant random walk, obtains when the t times iteration ant in elite
Random walk around ant lion
Step 6.3.3, by formulaIt is common by wheel disc roulette wheel strategy and elite ant lion to calculate every ant
Walking about under effect, updates the position f (w of anti,bi)；
Step 6.4, the fitness value for calculating every ant is realized by step 6.4.1 to step 6.4.6:
Each ant individual in ant population is divided into the input weight w that size is L × n by step 6.4.1i, size
It is the hidden layer biasing b of L × 1i；
Step 6.4.2, by formulaHidden layer output matrix H is calculated,
Middle g () indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.4.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix；
Step 6.4.4, the segmentation and classification device obtained by input feature value P training, segmentation and classification device include input weight,
Hidden layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.4.5；
Step 6.4.6, calculates the fitness value of ant, i.e. the segmentation of testing feature vector P' three-dimensional grid model is accurate
Degree, the accuracy of separation are the ratio between the grid surface number being correctly marked in grid model and total grid surface number；
Step 6.5, elite ant lion is updated, is realized by step 6.5.1 to step 6.5.2:
Step 6.5.1, ant has better fitness value than ant lion, by formulaAnt lion is updated into the position f (w to captured anti,bi), conversely, then ant
The position of lion remains unchanged；
Step 6.5.2, ant lion are more preferable than elite ant lion fitness value, the position of elite ant lion are updated, conversely, then elite ant
The position of lion remains unchanged；
Step 6.6, judge whether ant lion optimization algorithm reaches maximum number of iterations ImaxIf reached, elite ant is just exported
The accuracy of separation and the position of the corresponding adaptive optimal control angle value of lion namely testing feature vector P' three-dimensional grid model, Ye Jiji
Limit learning machine parameter combination f (w to be optimizedi,bi), otherwise the number of iterations t adds 1, jumps back to step 6.3 and continues to execute, until reaching
The maximum number of iterations I of algorithmmax；
Step 7, the optimal segmentation and classification device obtained based on step 6 training, and to the test feature obtained based on step 4
Vector P' is split test, obtains the optimal accuracy of separation of testing feature vector P' three-dimensional grid model, and it is right therewith to generate
The three-dimensional grid model segmentation result .seg file answered, and obtain visualization result.
The beneficial effects of the present invention are:
Compared with prior art, combining step 1 to step 7, remarkable advantage of the invention is: utilizing ant lion optimization algorithm
Middle ant by elite ant lion and roulette strategy double influence, iteration update simultaneously optimal solution is assigned to elite ant lion, thus
The input weight and hidden layer bias matrix that extreme learning machine generates at random are improved and optimizated, training obtains a high-precision segmentation
Classifier tests Princeton model.Compared to the work of forefathers, Airplane, Ant, Chair, Octopus,
On 6 class model data set of Teddy and Fish, model algorithm time-consuming 1000s or so of the training dough sheet number between 20k-30k, and
Dividing method than being not optimised obtains the higher accuracy of separation, and the highest accuracy of separation reaches 99.49%.
Detailed description of the invention
Fig. 1 is the overall flow figure of dividing method of the present invention；
Fig. 2 is basic extreme learning machine algorithm principle figure；
The convergence figure of Fig. 3 Airplane model aircraft different population scale；
The convergence figure of Fig. 4 Octopus octopus model different population scale；
Fig. 5 is Airplane model aircraft segmentation result figure；
Fig. 6 is Ant ant model segmentation result figure；
Fig. 7 is Chair chair model segmentation result figure；
Fig. 8 is Octopus octopus model segmentation result figure；
Fig. 9 is Teddy teddy bear model segmentation result figure；
Figure 10 is Fish fish model segmentation result figure.
Specific implementation method
Present invention is further described in detail with example with reference to the accompanying drawing.
As shown in Figure 1, being that the present invention is based on the streams of the three-dimensional grid model dividing method of the extreme learning machine of ant lion optimization
Cheng Tu, can summary be step 1 to step 7:
Step 1, for Princeton model data concentrate Airplane, Ant, Chair, Octopus, Teddy,
6 class three-dimensional grid model of Fish carries out dough sheet feature extraction to each three-dimensional grid model, and is normalized, normalizing
Change between [- 1,1], eliminate dimension impact, wherein dough sheet feature, comprising: dough sheet curvature feature, PCA feature, in shape under
Literary feature, average geodesic distance feature, shape characteristics of diameters.
Step 2, Airplane, Ant, Chair, Octopus, Teddy, Fish Princeton model data concentrated
6 class three-dimensional grid models, are divided into training dataset and test data set in proportion respectively, due in the data set of Princeton
6 class three-dimensional grid model of Airplane, Ant, Chair, Octopus, Teddy, Fish, the quantity of every class grid model are all
20, random selection 95% namely 19 models are as training dataset, for training segmentation and classification device, remaining 5%,
That is 1 model is as test data set, for testing to trained segmentation and classification device.
Step 3, the input feature value that obtained every class three-dimensional grid model training dataset is divided based on step 2 is established
P, specifically: every class three-dimensional grid model training data is concentrated to the model for including, in step 1 the dough sheet after normalized
Feature merges, and forms input feature value P, is expressed as P={ (x for the input feature value P that sample number is Mj,tj),j
=1,2 ..., M }, wherein xj=(xj1,xj2,…,xjn)T∈Rn, indicate to be directed to the extracted dough sheet feature of three-dimensional grid model, n
For the attribute size of input feature value P, tj=(tj1,tj2,…,tjm)T∈RmOr it is expressed as matrixIt represents
The label that three-dimensional grid model is beaten.
Step 4, the testing feature vector that obtained every class three-dimensional grid model test data set is divided based on step 2 is established
P', specifically: the dough sheet feature after the three-dimensional grid model of test data concentration in step 1 normalized is formed into test
Feature vector P'.
Step 5, the size of the hidden neuron L parameter of extreme learning machine model is set, specifically: by hidden neuron
Number L is set as 300, and basic extreme learning machine algorithm principle by step 5.1 to step 5.3 as shown in Fig. 2, realized:
Step 5.1, random to generate input weight wiB is biased with hidden layeri；
Step 5.2, by formulaHidden layer output matrix H is calculated, wherein
G () indicates activation primitive；
Step 5.3, by formulaOutput weight matrix is calculated, wherein H- is H generalized inverse matrix, matrixRepresent the label that three-dimensional grid model is beaten.
Step 6, extreme learning machine is optimized using ant lion optimization algorithm, first in selection Princeton data set
The model of 2 classifications of Airplane, Octopus carries out test experiments, Population Size N is respectively set to 10,20 and 30, such as
Fig. 3-Fig. 4, experiment have counted the convergence of this 2 kinds of models, 50 iteration different population scale respectively, due to segmentation side of the invention
The runing time of method can double to increase with the increase of population scale, while consider runing time and the accuracy of separation, work as kind
Group scale N is when being set as 20, we at runtime between good balance is achieved between the accuracy of separation, when iterating to the 25th
After secondary or so, accuracy of separation kept stable, so maximum number of iterations I in setting ant lion optimization algorithmmaxFor 25 Hes
Population scale N is 20, using the input feature value P obtained based on step 3 as input data, is input to the limit of ant lion optimization
It is trained in learning machine (ALO-ELM) model, training obtains optimal segmentation and classification device, and to being obtained based on step 4
Testing feature vector P' is split the specific of test and executes operation, is realized by step 6.1 to step 6.6:
Step 6.1, random initializtion ant and ant lion population, according to formulaIn limitation
The size of condition, random initializtion ant and ant lion population is N × L × (n+1), each ant and ant lion individual in population
Position indicates extreme learning machine parameter combination f (w to be optimizedi,bi)；
Step 6.2, the fitness value of every ant lion is calculated, and optimal is considered as elite ant lion, extremely by step 6.2.1
Step 6.2.7 is realized:
Each ant lion individual in ant lion population is divided into the input weight w that size is L × n by step 6.2.1i, size
It is the hidden layer biasing b of L × 1i；
Step 6.2.2, by formulaHidden layer output matrix H is calculated,
Middle g () indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.2.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix, matrixRepresent the label that three-dimensional grid model is beaten；
Step 6.2.4, the segmentation and classification device obtained by input feature value P training, wherein segmentation and classification device includes input
Weight, hidden layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.2.5；
Step 6.2.6, calculates the fitness value of ant lion, i.e. the segmentation of testing feature vector P' three-dimensional grid model is accurate
Degree, the accuracy of separation are the ratio between the grid surface number being correctly marked in grid model and total grid surface number；
Step 6.2.7 compares the size of the fitness value of each individual in ant lion population, from big to small according to fitness value
Sequence, is assigned to elite ant lion for optimal；
Step 6.3, t=1 is enabled, into iterative cycles, updates the position f (w of anti,bi), by step 6.3.1 to step
6.3.3 it realizes:
Step 6.3.1 calculates influence of the roulette strategy to ant random walk, obtains roulette strategy and changes at the t times
For when the random walk that selects
Step 6.3.2 calculates influence of the elite ant lion to ant random walk, obtains when the t times iteration ant in elite
Random walk around ant lion
Step 6.3.3, by formulaIt is common by wheel disc roulette wheel strategy and elite ant lion to calculate every ant
Walking about under effect, updates the position f (w of anti,bi)；
Step 6.4, the fitness value for calculating every ant is realized by step 6.4.1 to step 6.4.6:
Each ant individual in ant population is divided into the input weight w that size is L × n by step 6.4.1i, size
It is the hidden layer biasing b of L × 1i；
Step 6.4.2, by formulaHidden layer output matrix H is calculated,
Middle g () indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.4.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix；
Step 6.4.4, the segmentation and classification device obtained by input feature value P training, segmentation and classification device include input weight,
Hidden layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.4.5；
Step 6.4.6, calculates the fitness value of ant, i.e. the segmentation of testing feature vector P' three-dimensional grid model is accurate
Degree, the accuracy of separation are the ratio between the grid surface number being correctly marked in grid model and total grid surface number；
Step 6.5, elite ant lion is updated, is realized by step 6.5.1 to step 6.5.2:
Step 6.5.1, ant has better fitness value than ant lion, by formulaAnt lion is updated into the position f (w to captured anti,bi), conversely, then ant
The position of lion remains unchanged；
Step 6.5.2, ant lion are more preferable than elite ant lion fitness value, the position of elite ant lion are updated, conversely, then elite ant
The position of lion remains unchanged；
Step 6.6, judge whether ant lion optimization algorithm reaches maximum number of iterations ImaxIf reached, elite ant is just exported
The accuracy of separation and the position of the corresponding adaptive optimal control angle value of lion namely testing feature vector P' three-dimensional grid model, Ye Jiji
Limit learning machine parameter combination f (w to be optimizedi,bi), otherwise the number of iterations t adds 1, jumps back to step 6.3 and continues to execute, until reaching
The maximum number of iterations I of algorithmmax；
Step 7, the optimal segmentation and classification device obtained based on step 6 training, and to the test feature obtained based on step 4
Vector P' is split test, obtains the optimal accuracy of separation of testing feature vector P' three-dimensional grid model, and it is right therewith to generate
The three-dimensional grid model segmentation result .seg file answered, and obtain visualization result, such as Fig. 5-Figure 10, be respectively about
The segmentation result of 6 class three-dimensional grid model of Airplane, Ant, Chair, Octopus, Teddy, Fish.It is visual in the present invention
Change result and Chen X, journey used in Golovinskiy A, Funkhouser T.A benchmark for 3D mesh fruit
Sequence is identical.
For the advantage for showing dividing method of the present invention, experiment segmentation result of the invention is learnt with based on the basic limit
Threedimensional model dividing method CGF [2014], grid model dividing method ATG [2015] and base based on deep layer convolutional network
It is compared in the threedimensional model dividing method CEA [2017] of Fast Learning.
The dividing method of the present invention of table 1 is compared with other automatic Segmentation accuracy based on supervision
From table 1 it follows that dividing method of the present invention has very big promotion in contrast to CGF [2014] segmentation precision, than text
CEA [2017] is offered also to improve.In addition to this, although dividing method partial segmentation result of the present invention is relatively lower than ATG
[2015], it but on time performance, needs to spend 1000s or so in the dough sheet number of training 20k-30k.Same dough sheet number, phase
Than the method time-consuming 4h or so in ATG [2015], advantage is shown.In general, dividing method of the present invention is excellent by colony intelligence
The thought ant lion optimization algorithm of change is applied to the segmentation research of grid model, using this basic model of extreme learning machine,
Deep neural network training time too long problem is effectively prevented, and compared with the work of forefathers, the accuracy of separation keeps higher
Level, algorithm or effective and feasible.
Above embodiments are not limited to the technical solution of the embodiment itself, can be incorporated between embodiment new
Embodiment.The above embodiments are merely illustrative of the technical solutions of the present invention and is not intended to limit it, all without departing from the present invention
Any modification of spirit and scope or same replacement, shall fall within the scope of the technical solution of the present invention.
Claims (10)
1. a kind of three-dimensional grid model dividing method of the extreme learning machine based on ant lion optimization, which is characterized in that comprising following
Step:
Step 1, Airplane, Ant, Chair, Octopus, Teddy, Fish6 class concentrated for Princeton model data
Three-dimensional grid model carries out dough sheet feature extraction to each three-dimensional grid model, and is normalized；
Step 2, Airplane, Ant, Chair, Octopus, Teddy, Fish6 class three Princeton model data concentrated
Grid model is tieed up, is divided into training dataset and test data set in proportion respectively；
Step 3, the input feature value P that obtained every class three-dimensional grid model training dataset is divided based on step 2 is established；
Step 4, the testing feature vector P' that obtained every class three-dimensional grid model test data set is divided based on step 2 is established；
Step 5, the size of the hidden neuron L parameter of extreme learning machine model is set；
Step 6, extreme learning machine is optimized using ant lion optimization algorithm, maximum number of iterations I in ant lion optimization algorithm is setmaxWith
The size of population scale N is input to the pole of ant lion optimization using the input feature value P obtained based on step 3 as input data
Be trained in limit learning machine model, training obtains optimal segmentation and classification device, and to the test feature obtained based on step 4 to
Amount P' is split test；
Step 7, the optimal segmentation and classification device obtained based on step 6 training, and to the testing feature vector obtained based on step 4
P' is split test, obtains the optimal accuracy of separation of testing feature vector P' three-dimensional grid model, generates corresponding
Three-dimensional grid model segmentation result .seg file, and obtain visualization result.
2. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 1, the dough sheet feature, comprising: dough sheet curvature feature, Shape context feature, is averaged at PCA feature
Geodesic distance feature, shape characteristics of diameters, the normalized normalize between [- 1,1], eliminate dimension impact.
3. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 2, Airplane, Ant that Princeton model data is concentrated, Chair, Octopus, Teddy,
Fish6 class three-dimensional grid model, is divided into training dataset and test data set in proportion respectively, specifically: Princeton number
According to concentration Airplane, Ant, Chair, Octopus, Teddy, Fish6 class three-dimensional grid model, the quantity of every class grid model
It is all 20, random selection 95% namely 19 models are remaining for training segmentation and classification device as training dataset
5% namely 1 model as test data set, for testing to trained segmentation and classification device.
4. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 3, it is special to establish the input for dividing obtained every class three-dimensional grid model training dataset based on step 2
Vector P is levied, specifically: every class three-dimensional grid model training data is concentrated to the model for including, in step 1 after normalized
Dough sheet feature merge, form input feature value P, for sample number be M input feature value P be expressed as P=
{(xj,tj), j=1,2 ..., M }, wherein xj=(xj1,xj2,…,xjn)T∈Rn, indicate extracted for three-dimensional grid model
Dough sheet feature, n are the attribute size of input feature value P, tj=(tj1,tj2,…,tjm)T∈RmOr it is expressed as matrixRepresent the label that three-dimensional grid model is beaten.
5. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 4, it is special to establish the test for dividing obtained every class three-dimensional grid model test data set based on step 2
Vector P' is levied, specifically: by the dough sheet feature group after the three-dimensional grid model of test data concentration in step 1 normalized
At testing feature vector P'.
6. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 5, the size of the hidden neuron L parameter of extreme learning machine model is set, specifically: by hidden layer mind
300 are set as through first number L,；The extreme learning machine rudimentary algorithm is realized by step 5.1 to step 5.3:
Step 5.1, random to generate input weight wiB is biased with hidden layeri；
Step 5.2, by formulaHidden layer output matrix H is calculated, wherein g ()
Indicate activation primitive；
Step 5.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix, matrix
Represent the label that three-dimensional grid model is beaten.
7. the three-dimensional grid model dividing method of the extreme learning machine according to claim 1 based on ant lion optimization, special
Sign is: in the step 6, optimizing extreme learning machine using ant lion optimization algorithm, greatest iteration in ant lion optimization algorithm is arranged
Number ImaxIt is 20 for 25 and population scale N, using the input feature value P obtained based on step 3 as input data, is input to
It is trained in the extreme learning machine model of ant lion optimization, training obtains optimal segmentation and classification device, and obtains to based on step 4
Testing feature vector P' be split test specifically execute operation, realized by step 6.1 to step 6.6:
Step 6.1, random initializtion ant and ant lion population, according to formulaIn limitation item
The size of part, random initializtion ant and ant lion population is N × L × (n+1), the position of each ant and ant lion individual in population
Setting indicates extreme learning machine parameter combination f (w to be optimizedi,bi)；
Step 6.2, the fitness value of every ant lion is calculated, and optimal is considered as elite ant lion；
Step 6.3, t=1 is enabled, into iterative cycles, updates the position f (w of anti,bi)；
Step 6.4, the fitness value of every ant is calculated；
Step 6.5, elite ant lion is updated, is realized by step 6.5.1 to step 6.5.2:
Step 6.5.1, ant has better fitness value than ant lion, by formulaAnt lion is updated into the position f (w to captured anti,bi), conversely, then ant
The position of lion remains unchanged；
Step 6.5.2, ant lion be more preferable than elite ant lion fitness value, updates the position of elite ant lion, conversely, then elite ant lion
Position remains unchanged；
Step 6.6, judge whether ant lion optimization algorithm reaches maximum number of iterations ImaxIf reached, elite ant lion pair is just exported
The accuracy of separation of the adaptive optimal control angle value namely testing feature vector P' three-dimensional grid model answered and position namely the limit
Habit machine parameter combination f (w to be optimizedi,bi), otherwise the number of iterations t adds 1, jumps back to step 6.3 and continues to execute, until reaching algorithm
Maximum number of iterations Imax。
8. the three-dimensional grid model dividing method of the extreme learning machine according to claim 7 based on ant lion optimization, special
Sign is: in the step 6.2, calculating the fitness value of every ant lion, and optimal is considered as elite ant lion, specifically by step
6.2.1 it is realized to step 6.2.7:
Each ant lion individual in ant lion population is divided into the input weight w that size is L × n by step 6.2.1i, size be L ×
1 hidden layer biases bi；
Step 6.2.2, by formulaHidden layer output matrix H is calculated, wherein g
() indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.2.3, by formulaOutput weight matrix is calculated, wherein H-It is H generalized inverse matrix, matrixRepresent the label that three-dimensional grid model is beaten；
Step 6.2.4, the segmentation and classification device obtained by input feature value P training, wherein segmentation and classification device includes input weight,
Hidden layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.2.5；
Step 6.2.6 calculates the fitness value of ant lion, the i.e. accuracy of separation of testing feature vector P' three-dimensional grid model, point
Cutting accuracy is the ratio between the grid surface number being correctly marked in grid model and total grid surface number；
Step 6.2.7, compares the size of the fitness value of each individual in ant lion population, arranges from big to small according to fitness value
Optimal is assigned to elite ant lion by sequence.
9. the three-dimensional grid model dividing method of the extreme learning machine according to claim 7 based on ant lion optimization, special
Sign is: in the step 6.3, enabling t=1, into iterative cycles, updates the position f (w of anti,bi), specifically by step
6.3.1 it is realized to step 6.3.3:
Step 6.3.1 calculates influence of the roulette strategy to ant random walk, obtains roulette strategy in the t times iteration
The random walk of selection
Step 6.3.2 calculates influence of the elite ant lion to ant random walk, obtains when the t times iteration ant in elite ant lion
The random walk of surrounding
Step 6.3.3, by formulaIt calculates every ant and passes through wheel disc roulette wheel strategy and elite ant lion collective effect
Under walk about, update the position f (w of anti,bi)。
10. the three-dimensional grid model dividing method of the extreme learning machine according to claim 7 based on ant lion optimization, special
Sign is: in the step 6.4, the fitness value of every ant calculated, is specifically realized by step 6.4.1 to step 6.4.6:
Each ant individual in ant population is divided into the input weight w that size is L × n by step 6.4.1i, size be L ×
1 hidden layer biases bi；
Step 6.4.2, by formulaHidden layer output matrix H is calculated, wherein g
() indicates that activation primitive, the activation primitive that this method uses are S type function；
Step 6.4.3, by formulaOutput weight matrix is calculated, wherein H- is H generalized inverse matrix；
Step 6.4.4, the segmentation and classification device obtained by input feature value P training, segmentation and classification device include input weight, are implied
Layer biasing, exports weight, predicts the label of each dough sheet of testing feature vector P' three-dimensional grid model；
The label that prediction obtains is compared with the label of standard, counts the number of correct labeling by step 6.4.5；
Step 6.4.6 calculates the fitness value of ant, the i.e. accuracy of separation of testing feature vector P' three-dimensional grid model, point
Cutting accuracy is the ratio between the grid surface number being correctly marked in grid model and total grid surface number.
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