CN105654187A - Grid binary tree method of control system midpoint locating method - Google Patents
Grid binary tree method of control system midpoint locating method Download PDFInfo
- Publication number
- CN105654187A CN105654187A CN201510970110.5A CN201510970110A CN105654187A CN 105654187 A CN105654187 A CN 105654187A CN 201510970110 A CN201510970110 A CN 201510970110A CN 105654187 A CN105654187 A CN 105654187A
- Authority
- CN
- China
- Prior art keywords
- lineoid
- eigenwert
- subregion
- node
- net region
- 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
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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
A grid binary tree method is divided into two main phases-an offline preprocessing phase and an online computation phase. The theory of multi-parameter secondary programming is introduced in the offline preprocessing phase. A computer can autonomously divide the state space of a control system into convex partitions and obtains control rate corresponding to each partition through computation. Then we construct a hash table grid area polytope according to division parameters and construct a binary tree in a grid area in which a conflict exists. In the online computation phase, firstly the located grid area is rapidly determined according to the coordinates of state points, the control rate of the state points is obtained through screening of the constructed binary tree or is directly obtained, and control output quantity of the system is obtained through simple linear operation.
Description
Technical field
The present invention be directed to the optimization of independent positioning method in explicit model predictive control, this grid Binomial Trees, relative to traditional Binomial Trees, only needs in the face of less polytope subregion scale, reduces pretreated complexity, greatly reduce pretreatment time. It also solves in Ha Xibiaofa the conflict problem existed simultaneously, improves Ha Xibiaofa online computing time. In this performance of memory space requirements, also meet us to the requirement of Controlling System.
Background technology
There is on-line optimization repeatedly in traditional Model Predictive Control to calculate, it causes that controller load is overweight and inefficiency. In order to address these problems, before and after 2002 the scholar such as ManfredMorari and AlbertoBemporad to introduce multiparameter quadratic programming theoretical, establish explicit model forecast Control Algorithm system. It mainly utilizes the affine rule of the segmentation of model predictive control system inherence, the information such as model according to control object, constraint, performance requriements, by multiparameter quadratic programming (multi-parametricQuadraticProgram, mp-QP) system state space is divided into subregion convex one by one and precomputes optimum control rate corresponding on each subregion. This means that in traditional Model Predictive Control, complicated time-consuming on-line optimization process completes before being advanceed to Controlling System actual motion, and only needs subregion residing for the current state point of certainty annuity during on-line Control, can obtain corresponding optimum control rate. The efficiency that this kind searches computing calculates far above on-line optimization repeatedly, and the real-time performance of Controlling System is greatly improved, and also reduces the requirement to Controlling System software and hardware simultaneously.
Can know that the main task of explicit model predictive control control stage solves point location problem exactly according to introduction above. As its name suggests, which subregion the state point judging in space exactly that point location problem refers to is in. Here subregion refers to the convex one by one subregion being divided into by state space by multiparameter quadratic programming (mp-QP), it is determined that namely the subregion object residing for point obtains this subregion optimum control rate, realizes system optimal control through simple conversion. The performance of independent positioning method that we adopt is directly connected to the performance of explicit model Predictive Control System, here the performance of independent positioning method refer to storage space shared by data, the off-line calculation time and online computing time three aspects.
Traditional independent positioning method has method of directly searching, can reach zone method, Ha Xibiaofa, Binomial Trees etc., and the open source literature of these methods of detail has many, just repeats no more here. Although they also reality can solve point location problem effectively, but in performance, can not meet our demand for control. Traditional Binomial model is compared with other independent positioning method, it has, on memory space requirements and online search efficiency, the advantage that cannot be equal to, but its pretreatment time but can not meet the requirement of our Controlling System, the On-line efficiency that traditional Ha Xibiaofa represents also makes us flatter. Here we just wish to propose a kind of new independent positioning method, and it needs the advantage retaining tradition Binomial Trees and Ha Xibiaofa, also to be had original performance on memory space requirements simultaneously.
Summary of the invention
The present invention want customer service conventional point localization method above-mentioned shortcoming, it provides a kind of grid Binomial Trees. It is not only complete remains tradition Binomial Trees and the low pretreatment time that represents of Ha Xibiaofa and high On-line efficiency, and how much it also inherits the advantage of Binomial Trees in memory space requirements simultaneously.
The essence of point location is exactly determining certain any residing subregion in space, then obtaining this zonal control rate and realize control effects. In the preprocessing process of Binomial Trees, the operation of complicated and time consumption picks out one group of combination the most suitable from a large amount of partition boundaries lineoid to set up binary tree the most, this crosses range request to be performed calculating repeatedly and contrast when setting up each node of binary tree, and calculated amount increases along with the dimension degree of subregion and quantity exponentially. And step the most time-consuming in Ha Xibiaofa determines state point place subregion by directly searching when surely belonging to on-line stage. So we are just in conjunction with two methods here, select the essence, discarding dross, it is proposed that grid Binomial Trees.
Grid Binomial Trees is divided into two main stage off-line pretreatment stages and online calculation stages. It is theoretical that off-line pretreatment stage introduces multiparameter quadratic programming, calculate function voluntarily the state space of Controlling System to be divided into subregion convex one by one and calculate the inverse amplification factor that each subregion is corresponding, then we build Ha Xibiao net region polytope according to division parameter, build binary tree in the net region that there is conflict. First online calculation stages determines net region, place fast according to state point coordinate, and the binary tree through setting up is screened or directly obtains state point inverse amplification factor, is obtained the control work output of system by simple linear computing.
The grid Binomial Trees of Controlling System mid point orientation problem of the present invention, specifically comprises the following steps:
Step 1. grid Binomial Trees off-line preprocessing process
1.1, introduce multiparameter quadratic programming in the controls, system state space is divided into subregion convex one by one, and calculates the inverse amplification factor that each subregion is corresponding, be kept in FG array.
1.2, calculate synonym subregion by the formula determining synonym subregion and divide into groups, each group of synonym subregion only retains a characteristic value data, thus eliminating the need the redundant data in eigenwert array FG.
In space, the division of subregion is according to having a little identical eigenwert in the same subregion of eigenwert. Subregion equal for eigenwert is called synonym subregion by us. In explicit model predictive control, subregion eigenwert (being the inverse amplification factor of explicit model predictive control here) is called as FG matrix.Such as certain explicit model predictive control output dimension degree is the FG matrix of the two-dimensional state space partition zone P of 1:
FG1=[f11f12g1](1)
The FG matrix of adjacent another subregion Q is:
FG2=[f21f22g2](2)
If meeting:
(f11-f21)2+(f12-f22)2+(g1-g2)2�ܦ�(3)
Wherein (3) formula is the formula determining synonym subregion, f and g is the element of composition characteristic value matrix, and it is calculate by eigenwert and state vector that the control that we need exports. When �� is a minimum positive number, then thinking that P and Q is synonym, they are synonym subregion each other.
1.3, calculate according to division parameter and breathe out uncommon function, the data obtained are recorded in an array, we are array called after Fhash. Breathe out uncommon function as shown in the formula:
Here N represents and divides parameter, a and b is the boundary coordinate on certain dimension degree, the data that we need to be recorded in array for-a withJust residing Ha Xibiao net region can be determined fast by state point coordinate X during online calculation stages.
1.4, according to division parametric configuration Ha Xibiao net region polytope, choose first net region in order with subregion asks friendship, add up the eigenwert quantity in this net region.
1.5, judge whether this net region exists conflict. In net region eigenwert quantity be greater than 1 be exist conflict. If there is conflict, jumping to 1.7, starting in net region, set up binary tree, if there is not conflict, entering next step.
1.6, judge whether this net region is in completely to outside image space. If net region is not crossing with any one subregion, then directly in Hash array, corresponding position is recorded as 0. If only there is a kind of eigenwert in net region, then directly in Hash array, recording feature value in corresponding position is numbered. 1.19 are jumped to after completing this step.
1.7, in Hash array, first insert y-bend root vertex address, and it is labeled as negative value. Then remove the relevant lineoid of net region neutral line and the outside border to image space, do not select them as lineoid to be selected. It is all divide by lineoid here to the subregion one by one in image space, the principle of Binomial Trees is exactly the position relation at node place judging point and lineoid one by one, determine state point is in which side of lineoid, enter next node after getting rid of the subregion of nearly half to continue to judge, finally obtain subregion residing for state point. Therefore with being unnecessary to the outside border of image space as node basis for estimation, its side or whole to image space, does not have excretion.
1.8, by subregion by eigenwert (eigenwert here is inverse amplification factor) grouping, the subregion that eigenwert is identical is one group, and identical eigenwert is combined into data.
1.9, calculate the limit coordinate in each component district, and eliminate the repetition coordinate in each group of limit coordinate, enter root node.
1.10, from present node lineoid to be selected, extract first lineoid.
1.11, the eigenwert quantity of statistics lineoid both sides. This statistics method be set up the important step of binary tree, it without the need to judging all subregion limits can add up the eigenwert quantity of lineoid both sides, greatly shorten pretreatment time. Key step is as follows:
A, is loaded into the subregion limit data of the characteristic grouping equal by eigenwert.
B, is loaded into and waits to judge lineoid, extracts first group of first limit coordinate, and lineoid both sides are defined as Hp-and Hp+ by respectively, and the eigenwert quantity of both sides is respectively m and n, and Schilling m and n is 0.
C, whether Lf and Rf is in the mark of Hp-and Hp+ by us as limit, and value is that 0 representative is false, and value is that 1 representative is true.Schilling Lf and Rf is 0.
D, judges the position relation of limit and lineoid. For lineoid Hp={x | hx=k}, if some x meets hx��k, then thinks that an x is positioned at Hp-, otherwise is positioned at Hp+. Wherein h and k is lineoid expression argument, and x judges state point coordinate for waiting.
E, if limit is positioned at Hp-, makes Lf=1, jumps to g, otherwise carries out next step.
F, judges whether limit is positioned at Hp+, if very, making Rf=1. Otherwise jump to h.
G, judges whether Rf=1 and Lf=1 sets up simultaneously, if false, carries out next step, if very, jumps to i.
H, judges whether it is that this organizes last limit, if false, extract the next limit coordinate of this group, and jumps to d. If true, if the value of Lf=1, m adds 1, if the value of Rf=1, n adds 1.
I, judges whether this group is last group limit data, if false, extract next group first limit coordinate, and jumps to c, otherwise lineoid both sides eigenwert quantity has been added up.
1.12, judge whether this is last lineoid to be selected, if false, extract next lineoid to be selected, jump to 1.11 statistics lineoid both sides eigenwert quantity, if very, enter next step.
1.13, determine with reference to lineoid according to index. It is desirable that the binary tree degree of depth ground set up and node are few, we can not attempt all combinations and set up all possible binary tree, then choose best one. We only need to consider that node both sides (i.e. lineoid both sides) eigenwert quantity is roughly the same, then think that this lineoid compares and be suitable as with reference to lineoid. Description index is as follows:
J=(m+n)2+(m-n)2
M, n are respectively the eigenwert quantity being positioned at Hp-and Hp+, and J is more little, then think that this lineoid is more applicable and be called with reference to lineoid. Both sides eigenwert quantity sums describe to this binary tree node number it is contemplated that the difference of both sides eigenwert describes binary tree left and right subtree balance is expected.
1.14, judge whether left subtree has been set up. If true, jump to 1.16, otherwise enter next step.
1.15, pass to left child node by being positioned at the limit with reference to lineoid Hp-side, lineoid to be selected is removed and passes to left child node with reference to after lineoid, after entering left child node, jump to 1.10.
1.16, judge whether right subtree has been set up. If true, jump to 1.18, if false, enter next step.
1.17, pass to right child node by being positioned at the limit with reference to lineoid Hp+ side, lineoid to be selected is removed and passes to right child node with reference to after lineoid, after entering right child node, jump to 1.10.
1.18, return father's node, and judge whether binary tree has set up, if false, jump to 1.12, if very, preserve data, so far in net region, binary tree has been set up.
1.19, judge whether current operation net region is last net region. If not, then choose next net region, and itself and subregion are asked friendship, add up wherein eigenwert quantity, and jump to 1.5. If last net region, illustrate that pre-treatment step completes, end operation.
The online computation process of step 2. grid Binomial Trees
2.1, read object point coordinate, according to the uncommon function quick position in Kazakhstan to net region, place, and read corresponding record in Hash array.
2.2, judge whether record value is negative. If record value is negative, jump to 2.4, perform binary tree search. If record value is not negative, carry out next step.
2.3, judge whether record value is just.If not just, illustrating that record value is 0, this net region is in completely to, outside image space, state point is not in, in image space, directly exiting and search the stage online yet. If record value only exists a kind of eigenwert for just illustrating in net region, record value is eigenwert numbering, it is possible to directly obtaining eigenwert, the stage of searching terminates online.
2.4, record value negate is root node address, enters root node.
2.5, judge that object point and node place are with reference to lineoid relation. If object point is positioned at Hp-side, enter left child node, if object point is positioned at Hp+ side, enter right child node.
2.6, judge whether this node is last binary tree node, if false, jump to 2.5, if very, enter next step.
2.7, judging object point and the position relation of last reference lineoid, if being positioned at Hp-, choosing left side cotyledon, if being positioned at Hp+, choosing right side cotyledon. According to eigenwert numbering corresponding on leaf node, extracting eigenwert from eigenvalue matrix FG, the online calculation stages of point location completes.
It is an advantage of the invention that: at memory space requirements, off-line pretreatment time with search online and achieve extraordinary balance in time three performances. Compared to traditional Binomial Trees and Ha Xibiaofa, the present invention has low off-line pretreatment time and the online search efficiency of height, also achieves certain improvement in memory space requirements simultaneously.
Accompanying drawing explanation
Fig. 1 is the state space subregion schematic diagram of the present invention
Fig. 2 is the net region schematic diagram that the present invention exists conflict
Fig. 3 is the binary tree schematic diagram that the present invention sets up
Fig. 4 is the lineoid both sides eigenwert quantity decision flow chart of the present invention
Fig. 5 is the off-line pretreatment process figure of the present invention
Fig. 6 is the online calculation stages schema of the present invention
Table 1 is the same classical independent positioning method performance comparison of grid Binomial Trees of the present invention
Embodiment
Below in conjunction with accompanying drawing, the grid Binomial Trees step of the present invention is described further. With reference to accompanying drawing 1-6, table 1.
Grid Binomial Trees of the present invention, concrete steps are as follows:
The off-line preprocessing process of step 1. grid Binomial Trees, schema refers to Fig. 4 and Fig. 5
1.1, introduce multiparameter quadratic programming in the controls, system state space is divided into subregion convex one by one, and calculates the inverse amplification factor that each subregion is corresponding, be kept in FG array. State space subregion schematic diagram refers to Fig. 1.
1.2, calculate synonym subregion by the formula determining synonym subregion and divide into groups, each group of synonym subregion only retains a characteristic value data.
1.3, calculate according to division parameter and breathe out uncommon function, the data obtained are recorded in an array.
1.4, according to division parametric configuration Ha Xibiao net region polytope, choose first net region in order with subregion asks friendship, add up the eigenwert quantity in this net region.
1.5, judge whether this net region exists conflict, Fig. 2 is shown in by schematic diagram. In net region eigenwert quantity be greater than 1 be exist conflict. If there is conflict, jumping to 1.7, starting in net region, set up binary tree, if there is not conflict, entering next step.
1.6, judge whether this net region is in completely to outside image space. If net region is not crossing with any one subregion, then directly in Hash array, corresponding position is recorded as 0. If only there is a kind of eigenwert in net region, then directly in Hash array, recording feature value in corresponding position is numbered.1.19 are jumped to after completing this step.
1.7, Hash array is inserted y-bend root vertex address, and is labeled as negative value. Setting up binary tree in this net region, Fig. 3 is shown in by the binary tree schematic diagram set up.
1.8, by subregion by eigenwert grouping, the subregion that eigenwert is identical is one group, and identical eigenwert is combined into data.
1.9, calculate the limit coordinate in each component district, and eliminate the repetition coordinate in each group of limit coordinate, enter root node.
1.10, from present node lineoid to be selected, extract first lineoid.
1.11, the eigenwert quantity of statistics lineoid both sides.
1.12, judge whether this is last lineoid to be selected, if false, extract next lineoid to be selected, jump to 1.11 statistics lineoid both sides eigenwert quantity, if very, enter next step.
1.13, determine with reference to lineoid according to index. Consider that node both sides (i.e. lineoid both sides) eigenwert quantity is roughly the same, then think that this lineoid compares and be suitable as with reference to lineoid.
1.14, judge whether left subtree has been set up. If true, jump to 1.16, otherwise enter next step.
1.15, pass to left child node by being positioned at the limit with reference to lineoid side, lineoid to be selected is removed and passes to left child node with reference to after lineoid, after entering left child node, jump to 2.10.
1.16, judge whether right subtree has been set up. If true, jump to 1.18, if false, enter next step.
1.17, pass to right child node by being positioned at the limit with reference to another side of lineoid, lineoid to be selected is removed and passes to right child node with reference to after lineoid, after entering right child node, jump to 1.10.
1.18, return father's node, and judge whether binary tree has set up, if false, jump to 1.12, if very, preserve data, so far in net region, binary tree has been set up.
1.19, judge whether current operation net region is last net region. If not, then choose next net region, and itself and subregion are asked friendship, add up wherein eigenwert quantity, and jump to 1.5. If last net region, illustrate that pre-treatment step completes, end operation.
The online computation process of step 2. two grades of grid methods, schema refers to Fig. 5
2.1, read object point coordinate, according to the uncommon function quick position in Kazakhstan to net region, place, and read corresponding record in Hash array.
2.2, judge whether record value is negative. If record value is negative, jump to 2.4, perform binary tree search. If record value is not negative, carry out next step.
2.3, judge whether record value is just. If not just, illustrating that record value is 0, this net region is in completely to, outside image space, state point is not in, in image space, directly exiting and search the stage online yet. If record value only exists a kind of eigenwert for just illustrating in net region, record value is eigenwert numbering, it is possible to directly obtains eigenwert, exits and search the stage online.
2.4, record value negate is root node address, enters root node.
2.5, judge that object point and node place are with reference to lineoid relation.
2.6, judge whether this node is last binary tree node, if false, jump to 2.5, if very, enter next step.
2.7, judge the position relation of object point and last reference lineoid and obtain state point place subregion eigenwert accordingly, exit and search the stage online.
Case analysis
The present invention passes through the performance of two rank examples comparative grid Binomial Trees with classical independent positioning method, shows its superiority in memory space requirements, off-line pretreatment time, online computing time three.
Table 1 is the grid Binomial Trees of the present invention and the performance comparison of classical independent positioning method. Table 1 is Binomial Trees and the contrast of each algorithm performance fast
Being not difficult to find that grid Binomial Trees has had great improvement compared to traditional Binomial Trees on pretreatment time from table, also have no time compared with other independent positioning method allows more simultaneously. Simultaneously compared with Ha Xibiaofa, its average online computing time shortens widely, and its performance on memory space requirements is not poor yet simultaneously, it is possible to meet the demand of our Controlling System.
Claims (1)
1. the grid Binomial Trees of Controlling System mid point orientation problem, specifically comprises the following steps:
Step 1. grid Binomial Trees off-line pre-treatment;
1.1, introduce multiparameter quadratic programming in the controls, system state space is divided into subregion convex one by one, and calculates the inverse amplification factor that each subregion is corresponding, be kept in FG array;
1.2, calculate synonym subregion by the formula determining synonym subregion and divide into groups, each group of synonym subregion only retains a characteristic value data, thus eliminating the need the redundant data in eigenwert array FG;
In space, the division of subregion is according to having a little identical eigenwert in the same subregion of eigenwert; Subregion equal for eigenwert is called synonym subregion by us; In explicit model predictive control, subregion eigenwert (being the inverse amplification factor of explicit model predictive control here) is called as FG matrix; Such as certain explicit model predictive control output dimension degree is the FG matrix of the two-dimensional state space partition zone P of 1:
FG1=[f11f12g1](1)
The FG matrix of adjacent another subregion Q is:
FG2=[f21f22g2](2)
If meeting:
(f11-f21)2+(f12-f22)2+(g1-g2)2�ܦ�(3)
Wherein (3) formula is the formula determining synonym subregion, fijAnd gi(i, j=1,2) is the element of composition characteristic value matrix, and it is calculate by eigenwert and state vector that the control that we need exports; Definition �� is a minimum positive number, then think that P and Q is synonym, and they are synonym subregion each other;
1.3, calculate according to division parameter and breathe out uncommon function, the data obtained are recorded in an array, we are array called after Fhash; Breathe out uncommon function as shown in the formula:
Here N represents and divides parameter, a and b is the boundary coordinate on certain dimension degree, the data that we need to be recorded in array for-a withJust residing Ha Xibiao net region can be determined fast by state point coordinate X during online calculation stages;
1.4, according to division parametric configuration Ha Xibiao net region polytope, choose first net region in order with subregion asks friendship, add up the eigenwert quantity in this net region;
1.5, judge whether this net region exists conflict; In net region eigenwert quantity be greater than 1 be exist conflict; If there is conflict, jumping to 1.7, starting in net region, set up binary tree, if there is not conflict, entering next step;
1.6, judge whether this net region is in completely to outside image space; If net region is not crossing with any one subregion, then directly in Hash array, corresponding position is recorded as 0; If only there is a kind of eigenwert in net region, then directly in Hash array, recording feature value in corresponding position is numbered; 1.19 are jumped to after completing this step;
1.7, in Hash array, first insert y-bend root vertex address, and it is labeled as negative value; Then remove the relevant lineoid of net region neutral line and the outside border to image space, do not select them as lineoid to be selected; It is all divide by lineoid here to the subregion one by one in image space, the principle of Binomial Trees is exactly the position relation at node place judging point and lineoid one by one, determine state point is in which side of lineoid, enter next node after getting rid of the subregion of nearly half to continue to judge, finally obtain subregion residing for state point; Therefore with being unnecessary to the outside border of image space as node basis for estimation, its side or whole to image space, does not have excretion;
1.8, by subregion by eigenwert (eigenwert here is inverse amplification factor) grouping, the subregion that eigenwert is identical is one group, and identical eigenwert is combined into data;
1.9, calculate the limit coordinate in each component district, and eliminate the repetition coordinate in each group of limit coordinate, enter root node;
1.10, from present node lineoid to be selected, extract first lineoid;
1.11, the eigenwert quantity of statistics lineoid both sides; This statistics method be set up the important step of binary tree, it without the need to judging all subregion limits can add up the eigenwert quantity of lineoid both sides, greatly shorten pretreatment time; Key step is as follows:
A, is loaded into the subregion limit data of the characteristic grouping equal by eigenwert;
B, is loaded into and waits to judge lineoid, extracts first group of first limit coordinate, lineoid both sides are defined as Hp-and Hp+ respectively, and the eigenwert quantity of both sides is respectively m and n, and Schilling m and n is 0.
Whether c, be in the mark of Hp-and Hp+ using Lf and Rf as limit, and value is that 0 representative is false, and value is that 1 representative is true; Schilling Lf and Rf is 0;
D, judges the position relation of limit and lineoid; For lineoid Hp={x | hx=k}, if some x meets hx��k, then thinks that an x is positioned at Hp-, otherwise is positioned at Hp+; Wherein h and k is lineoid expression argument, and x judges state point coordinate for waiting;
E, if limit is positioned at Hp-, makes Lf=1, jumps to g, otherwise carries out next step;
F, judges whether limit is positioned at Hp+, if very, making Rf=1; Otherwise jump to h;
G, judges whether Rf=1 and Lf=1 sets up simultaneously, if false, carries out next step, if very, jumps to i;
H, judges whether it is that this organizes last limit, if false, extract the next limit coordinate of this group, and jumps to d; If true, if the value of Lf=1, m adds 1, if the value of Rf=1, n adds 1;
I, judges whether this group is last group limit data, if false, extract next group first limit coordinate, and jumps to c, otherwise lineoid both sides eigenwert quantity has been added up;
1.12, judge whether this is last lineoid to be selected, if false, extract next lineoid to be selected, jump to 1.11 statistics lineoid both sides eigenwert quantity, if very, enter next step;
1.13, determine with reference to lineoid according to index; Wish that binary tree degree of depth ground and the node of foundation are few, it is impossible to attempt all combinations and set up all possible binary tree, then choose best one; Only need to consider that node both sides (i.e. lineoid both sides) eigenwert quantity is roughly the same, then think that this lineoid compares and be suitable as with reference to lineoid; Description index is as follows:
J=(m+n)2+(m-n)2
M, n are respectively the eigenwert quantity being positioned at Hp-and Hp+, and J is more little, then think that this lineoid is more applicable and be called with reference to lineoid; Both sides eigenwert quantity sums describe to this binary tree node number it is contemplated that the difference of both sides eigenwert describes binary tree left and right subtree balance is expected;
1.14, judge whether left subtree has been set up; If true, jump to 1.16, otherwise enter next step;
1.15, pass to left child node by being positioned at the limit with reference to lineoid Hp-side, lineoid to be selected is removed and passes to left child node with reference to after lineoid, after entering left child node, jump to 1.10;
1.16, judge whether right subtree has been set up; If true, jump to 1.18, if false, enter next step;
1.17, pass to right child node by being positioned at the limit with reference to lineoid Hp+ side, lineoid to be selected is removed and passes to right child node with reference to after lineoid, after entering right child node, jump to 1.10;
1.18, return father's node, and judge whether binary tree has set up, if false, jump to 1.12, if very, preserve data, so far in net region, binary tree has been set up;
1.19, judge whether current operation net region is last net region; If not, then choose next net region, and itself and subregion are asked friendship, add up wherein eigenwert quantity, and jump to 1.5; If last net region, illustrate that pre-treatment step completes, end operation;
Step 2. grid Binomial Trees is in line computation;
2.1, read object point coordinate, according to the uncommon function quick position in Kazakhstan to net region, place, and read corresponding record in Hash array;
2.2, judge whether record value is negative; If record value is negative, jump to 2.4, perform binary tree search; If record value is not negative, carry out next step;
2.3, judge whether record value is just; If not just, illustrating that record value is 0, this net region is in, outside image space, state point is not in, in image space, directly exiting and search the stage online yet completely; If record value only exists a kind of eigenwert for just illustrating in net region, record value is eigenwert numbering, it is possible to directly obtaining eigenwert, the stage of searching terminates online;
2.4, record value negate is root node address, enters root node;
2.5, judge that object point and node place are with reference to lineoid relation; If object point is positioned at Hp-side, enter left child node, if object point is positioned at Hp+ side, enter right child node;
2.6, judge whether this node is last binary tree node, if false, jump to 2.5, if very, enter next step;
2.7, judging object point and the position relation of last reference lineoid, if being positioned at Hp-, choosing left side cotyledon, if being positioned at Hp+, choosing right side cotyledon; According to eigenwert numbering corresponding on leaf node, extracting eigenwert from eigenvalue matrix FG, the online calculation stages of point location completes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510970110.5A CN105654187A (en) | 2015-12-21 | 2015-12-21 | Grid binary tree method of control system midpoint locating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510970110.5A CN105654187A (en) | 2015-12-21 | 2015-12-21 | Grid binary tree method of control system midpoint locating method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105654187A true CN105654187A (en) | 2016-06-08 |
Family
ID=56477514
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510970110.5A Pending CN105654187A (en) | 2015-12-21 | 2015-12-21 | Grid binary tree method of control system midpoint locating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105654187A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153707A (en) * | 2017-05-12 | 2017-09-12 | 华中科技大学 | A kind of Hash table construction method and system for nonvolatile memory |
| CN107608214A (en) * | 2017-10-16 | 2018-01-19 | 浙江工业大学之江学院 | Multilevel splitting independent positioning method in Three Degree Of Freedom helicopter explicit model PREDICTIVE CONTROL |
| CN107817680A (en) * | 2017-10-10 | 2018-03-20 | 浙江工业大学之江学院 | Independent positioning method based on k d trees in helicopter explicit model PREDICTIVE CONTROL |
| CN108333927A (en) * | 2018-01-22 | 2018-07-27 | 浙江工业大学之江学院 | A kind of more cell space approximation explicit model forecast Control Algorithms of brshless DC motor |
| CN109359701A (en) * | 2018-11-28 | 2019-02-19 | 重庆邮电大学 | A kind of three-dimensional modeling data analytic method of extracted with high accuracy and Fast Classification |
| CN109960186A (en) * | 2017-12-25 | 2019-07-02 | 北京创昱科技有限公司 | Processing method, device, electronic equipment and the storage medium of control flow |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104375416A (en) * | 2014-08-21 | 2015-02-25 | 浙江工业大学 | Synonymy redundancy elimination grid method of point locating in control system |
-
2015
- 2015-12-21 CN CN201510970110.5A patent/CN105654187A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104375416A (en) * | 2014-08-21 | 2015-02-25 | 浙江工业大学 | Synonymy redundancy elimination grid method of point locating in control system |
Non-Patent Citations (1)
| Title |
|---|
| 谢作樟: "显式模型预测控制点定位算法的优化与实验研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153707A (en) * | 2017-05-12 | 2017-09-12 | 华中科技大学 | A kind of Hash table construction method and system for nonvolatile memory |
| CN107153707B (en) * | 2017-05-12 | 2020-08-14 | 华中科技大学 | Hash table construction method and system for nonvolatile memory |
| CN107817680A (en) * | 2017-10-10 | 2018-03-20 | 浙江工业大学之江学院 | Independent positioning method based on k d trees in helicopter explicit model PREDICTIVE CONTROL |
| CN107817680B (en) * | 2017-10-10 | 2020-07-17 | 浙江工业大学之江学院 | Point positioning method based on k-d tree in helicopter explicit model predictive control |
| CN107608214A (en) * | 2017-10-16 | 2018-01-19 | 浙江工业大学之江学院 | Multilevel splitting independent positioning method in Three Degree Of Freedom helicopter explicit model PREDICTIVE CONTROL |
| CN107608214B (en) * | 2017-10-16 | 2020-08-14 | 浙江工业大学之江学院 | Multi-stage grid point positioning method in three-degree-of-freedom helicopter explicit model predictive control |
| CN109960186A (en) * | 2017-12-25 | 2019-07-02 | 北京创昱科技有限公司 | Processing method, device, electronic equipment and the storage medium of control flow |
| CN108333927A (en) * | 2018-01-22 | 2018-07-27 | 浙江工业大学之江学院 | A kind of more cell space approximation explicit model forecast Control Algorithms of brshless DC motor |
| CN109359701A (en) * | 2018-11-28 | 2019-02-19 | 重庆邮电大学 | A kind of three-dimensional modeling data analytic method of extracted with high accuracy and Fast Classification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105654187A (en) | Grid binary tree method of control system midpoint locating method | |
| CN100456281C (en) | Data division apparatus, data division method | |
| CN103793467B (en) | Method for optimizing real-time query on big data on basis of hyper-graphs and dynamic programming | |
| CN112817312B (en) | Path planning method based on double search optimization algorithm | |
| CN102737114B (en) | MapReduce-based big picture distance connection query method | |
| CN102663514A (en) | Constrained optimization evolution algorithm based on feasible equilibrium mechanism | |
| CN109033340A (en) | A kind of searching method and device of the point cloud K neighborhood based on Spark platform | |
| CN107491508B (en) | Database query time prediction method based on recurrent neural network | |
| CN109074348A (en) | For being iterated the equipment and alternative manner of cluster to input data set | |
| CN110222824B (en) | Intelligent algorithm model autonomous generation and evolution method, system and device | |
| CN116304213B (en) | RDF graph database sub-graph matching query optimization method based on graph neural network | |
| CN108197186B (en) | Dynamic graph matching query method applied to social network | |
| CN108182243A (en) | A kind of Distributed evolutionary island model parallel method based on Spark | |
| CN116054144A (en) | Distribution network reconstruction method, system and storage medium for distributed photovoltaic access | |
| CN109635473B (en) | Heuristic high-flux material simulation calculation optimization method | |
| CN116662412A (en) | Data mining method for big data of power grid distribution and utilization | |
| CN104570759B (en) | The quick Binomial Trees of control system midpoint orientation problem | |
| CN107133281B (en) | Global multi-query optimization method based on grouping | |
| KR101515304B1 (en) | Reduce-side join query processing method for hadoop-based reduce-side join processing system | |
| CN115099309A (en) | Method for designing cost evaluation model for storage and index of graph data | |
| CN107105052A (en) | Heuristic web service composition method based on figure planning | |
| CN114021977A (en) | Typical composite scene reduction method and system for energy storage planning | |
| CN104503225A (en) | Control system midpoint positioning method | |
| CN104375416B (en) | The synonymous de-redundancy gridding method of control system midpoint orientation problem | |
| CN114580779B (en) | Block chain transaction behavior prediction method based on graph feature extraction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20160608 |
|
| WD01 | Invention patent application deemed withdrawn after publication |