CN108491641A - A kind of probability integration process parameter inversion method based on Quantum Annealing - Google Patents

A kind of probability integration process parameter inversion method based on Quantum Annealing Download PDF

Info

Publication number
CN108491641A
CN108491641A CN201810255950.7A CN201810255950A CN108491641A CN 108491641 A CN108491641 A CN 108491641A CN 201810255950 A CN201810255950 A CN 201810255950A CN 108491641 A CN108491641 A CN 108491641A
Authority
CN
China
Prior art keywords
parameter
iteration
probability
value
temperature
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
Application number
CN201810255950.7A
Other languages
Chinese (zh)
Inventor
魏涛
王磊
蒋创
方苏阳
李楠
池深深
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University of Science and Technology
Original Assignee
Anhui University of Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Anhui University of Science and Technology filed Critical Anhui University of Science and Technology
Priority to CN201810255950.7A priority Critical patent/CN108491641A/en
Publication of CN108491641A publication Critical patent/CN108491641A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Abstract

The present invention relates to a kind of probability integration process parameter inversion method based on Quantum Annealing, probability integral initial parameter value B given first0Temperature and transverse field change function, fluctuation range ± Δ B, the parameter maximum allowable step-length scale and interior cycle-index M of each probability integral parameter, secondly, by calculating the object function at the temperature and transverse field that continuously decrease, so that it is determined that optimized parameter solution at each temperature, optimized parameter solution is exported eventually by judging required precision or whether reaching minimum temperature and transverse field.This method is on the basis of inheriting simulated annealing advantage, the shortcomings that the thermal fluctuation mechanism of simulated annealing is replaced using quantum fluctuation mechanism, effectively overcomes simulated annealing.Relative to the probability integral parameter inversion method based on simulated annealing, this method effectively raises convergence rate and jumps out the possibility of locally optimal solution, enhances global optimization ability so that probability integral parametric inversion is more accurate, more reliable.

Description

A kind of probability integration process parameter inversion method based on Quantum Annealing
Technical field
The invention belongs to mine technology for deformation monitoring fields, and in particular to a kind of probability integration process parameter of Quantum Annealing Inversion method.
Background technology
Probability integration process is the prediction of mining subsidence method that the Chinese authority specifies, and probability integration process is sought based on field data Parameter is the core content of mining subsidence observation data processing.Probability function model is typical multi-parameter (8 parameters：Under Heavy coefficient q, main influence angle tangent tan β, displacement factor b, main propagation angle θ, upper deviation of inflection point Su, under turn Point offset distance Sd, left deviation of inflection point SlWith right deviation of inflection point Sr) complex nonlinear model, how to be based on terrene shift observing It is the difficult point of Probability Integral Method To Predicate Model application that field data of standing, which precisely seeks ginseng,.
Traditional probability integration process parameter acquiring method is mainly that characteristic point asks ginseng and least square fitting to seek ginseng.It is wherein special Sign point asks ginseng method simple, but characteristic point is often difficult to accurately choose, and seeks ginseng poor reliability.Least square fitting seeks ginseng method It is theoretical tight, but ask moduli type pair and probability integral initial parameter values requirement extremely stringent, if initial parameter values deviation true value is super Certain threshold value is crossed, moduli type is asked to lead to not diverging to seek ginseng, asks ginseng difficulty big.Since tradition asks ginseng method to be lacked there are above-mentioned Point, experts and scholars introduce nonlinear optimization algorithm carry out probability integration process parametric inversion, such as Pattern search, neural network, Support vector machines method, genetic algorithm, artificial bee colony algorithm, drosophila algorithm, particle cluster algorithm, and it is introduced into mining subsidence in the recent period The simulated annealing (abbreviation SA) in field etc..By being found to above-mentioned nonlinear optimization algorithm comparative studies：(1) above-mentioned non-thread Property optimization algorithm generally existing in probability integral parameter finding process it is different degrees of be easily absorbed in locally optimal solution problem, seek Probability integral parameter poor reliability；(2) SA algorithms have certain global optimization ability and (can to a certain degree reduce to parameter The required precision of initial value), and suitable for the probability integration process parametric inversion of arbitrary shape of working surface, but there are still convergence rates Slowly, the problems such as being easily absorbed in locally optimal solution.If SA algorithm the convergence speed can be improved, improve the possibility for jumping out locally optimal solution Property, enhance global optimization ability, then relative to other methods using SA algorithms carry out probability integration process parametric inversion with compared with Big superiority.
Related document shows：The Quantum annealing algorithm (QA) for merging quantum flood tide mechanism and simulated annealing theory was both good SA algorithm advantages are inherited, can also be accelerated to restrain by quantum tunneling effect, while improving the possibility for jumping out locally optimal solution, Quantum annealing cleverly overcomes the shortcomings that SA.
Invention content
Based on this, it is necessary in view of the above-mentioned problems, the present invention is proposed Quantum annealing for probability integral parametric inversion Thought, and specifically build the probability integration process parameter inversion method annealed based on quantum simulation.
Purpose according to the present invention provides a kind of probability integration process parameter inversion method based on Quantum Annealing, including Following steps：
Step 1 presets probability integral initial parameter value B0, temperature and transverse field change functionMinimum temperature and transverse field Tmin=Γmin, fluctuation range ± Δ the B of probability integral parameter, maximum allowable step Long scale and interior cycle-index M；
Step 2, interior loop iteration start, and calculate the target function value f of initial parameter0=Σ ((Wa0-Wp0)2+(Ua0-Up0 )2), by the maximum allowable step-length scale come calculating parameter value, set the object function of ith iteration as：fi=Σ ((Wpi-Wai)2+(Upi-Uai)2), the random number R and (j) between (0,1) is randomly generated, according to formula Bi+1(j)=Bi(j)+ (2-Rand (j)) Δ B (j) calculates the parameter value B after each parameter i+1 time iterationi+1, after judging i+1 time iteration Whether parameter is within the scope of parameter fluctuation, if not existing, takes Bi+1(j)=Bi(j)；Otherwise, after the completion of iteration, i+1 time is calculated The object function f of iterationi+1；
Step 3 calculates Δ f=fi+1-fiIf Δ f<0 or Δ f>0 andThen select i+1 subparameter value Bi+1Instead of ith parameter value Bi；Otherwise, still Select ith parameter value Bi；
Step 4 judges whether interior cycle-index m meets preset interior cycle-index M, if not satisfied, then entering step Suddenly (2) otherwise enter step (5)；
Step 5 judges whether to meet required precision, otherwise judge if satisfied, then stopping iteration output optimized parameter solution Temperature T and transverse field Г whether within the allowable range, if reducing temperature and transverse field, repeating step (2)-(4), otherwise jump Go out cycle, exports parametric solution at this time as optimized parameter solution.
Wherein, the probability integral parameter include subsidence factor q, it is main influence angle tangent tan β, displacement factor b, Angle of maximum subsidence θ, upper deviation of inflection point Su, lower deviation of inflection point Sd, left deviation of inflection point SlWith right deviation of inflection point Sr, B0= [q,tanβ,b,θ,Su,Sd,Sl,Sr], ± Δ B=[Δ q, Δ tan β, Δ b, Δ θ, Δ Su,ΔSd,ΔSl,ΔSr], t expressions change Generation number, i=1:N, N are number of parameters, Wai、UaiThe sinking of terrene shift observing point actual measurement and water respectively above working face Translation is dynamic, Wpi、UpiRespectively ith iteration when observation point prediction sinking and horizontal movement value,H0Indicate Hamiiton amounts when ground state,Indicate i-th of particle x-axis spin Pauli table As Γ (t) characterizes the transverse field of a variation, to cause the quantum between different conditions to be leaped.
Beneficial effects of the present invention：
Probability integral parameter inversion method based on Quantum annealing utilizes on the basis of inheriting simulated annealing advantage Quantum fluctuation mechanism replaces the thermal fluctuation mechanism of simulated annealing, effectively overcomes the shortcomings that simulated annealing is calculated.Relative to base In the probability integral parameter inversion method of simulated annealing, the probability integral parameter inversion method based on quantum simulation annealing is effective It is slow to improve convergence rate, enhances global optimization ability so that probability integral parametric inversion is more accurate, more reliable.
Description of the drawings
Fig. 1 is the Quantum Annealing reverse probability integration method parameter flow chart of the present invention
Fig. 2 is MPIPIMQA, MPIPIMSA sinking fitted figure of the present invention
Fig. 3 is MPIPIMQA, MPIPIMSA horizontal movement value fitted figure of the present invention
Specific implementation mode
The application is described in further detail below in conjunction with the accompanying drawings, it is necessary to it is indicated herein to be, implement in detail below Mode is served only for that the application is further detailed, and should not be understood as the limitation to the application protection domain, the field Technical staff can make some nonessential modifications and adaptations according to above-mentioned application content to the application.
Embodiment 1 is wrapped as shown in Figure 1, giving a kind of probability integration process parameter inversion method based on Quantum Annealing Include following steps：
Assuming that working face above terrene shift observing point actual measurement sinking and move horizontally respectively Wai、Uai, ith changes For when observation point prediction sinking and horizontal movement value be respectively Wpi、Upi, minimum with the difference quadratic sum of predicted value and observation Criterion, the then energy being calculated under certain state be,
Ei=fi=Σ ((Wpi-Wai)2+(Upi-Uai)2)
It is without the Hamilton amounts under outer force effect in Quantum annealing then,
H0=Ei+1-Ei=fi+1-fi
Step 1 presets probability integral initial parameter value B0, temperature and transverse field change functionThat is the same amount reduction simultaneously of temperature and transverse field, minimum temperature and transverse field Tmin=Γmin, probability integral ginseng Several fluctuation range ± Δ B, maximum allowable step-length scale and interior cycle-index M；
Step 2, interior loop iteration start, and calculate the target function value f of initial parameter0=Σ ((Wa0-Wp0)2+(Ua0-Up0 )2), by the maximum allowable step-length scale come calculating parameter value, set the object function of ith iteration as：fi=Σ ((Wpi-Wai)2+(Upi-Uai)2), the random number R and (j) between (0,1) is randomly generated, according to formula Bi+1(j)=Bi(j)+ (2-Rand (j)) Δ B (j) calculates the parameter value B after each parameter i+1 time iterationi+1, after judging i+1 time iteration Whether parameter is within the scope of parameter fluctuation, if not existing, takes Bi+1(j)=Bi(j)；Otherwise, after the completion of iteration, i+1 time is calculated The object function f of iterationi+1；
Step 3 calculates Δ f=fi+1-fiIf Δ f<0 or Δ f>0 andThen select i+1 subparameter value Bi+1Instead of ith parameter value Bi；Otherwise, still Select ith parameter value Bi；
Step 4 judges whether interior cycle-index m meets preset interior cycle-index M, if not satisfied, then entering step Suddenly (2) otherwise enter step (5)；
Step 5 judges whether to meet required precision, otherwise judge if satisfied, then stopping iteration output optimized parameter solution Temperature T and transverse field Г whether within the allowable range, if reducing temperature and transverse field, repeating step (2)-(4), otherwise jump Go out cycle, exports parametric solution at this time as optimized parameter solution.
Under normal circumstances, judged according to the above, required precision can be met, optimal solution can be obtained；But if precision It is required that excessively high, when temperature and transverse field reduction still cannot be satisfied required precision beyond allowable range, to avoid endless loop, this When jump out cycle, and current solution is considered parametric optimal solution.Here the sinking and move horizontally that required precision selects Small Mr. Yu's threshold value msn, the msn occurrence of error can be arranged according to requirement of engineering precision in fitting,
Wherein, the probability integral parameter include subsidence factor q, it is main influence angle tangent tan β, displacement factor b, Angle of maximum subsidence θ, upper deviation of inflection point Su, lower deviation of inflection point Sd, left deviation of inflection point SlWith right deviation of inflection point Sr, B0= [q,tanβ,b,θ,Su,Sd,Sl,Sr], ± Δ B=[Δ q, Δ tan β, Δ b, Δ θ, Δ Su,ΔSd,ΔSl,ΔSr], t expressions change Generation number, i=1:N, N are number of parameters, Wai、UaiThe sinking of terrene shift observing point actual measurement and water respectively above working face Translation is dynamic, Wpi、UpiRespectively ith iteration when observation point prediction sinking and horizontal movement value,H0Indicate Hamilton amounts when ground state,Indicate i-th of particle x-axis spin Pauli presentation. Herein, in order to simplify interparticle complex relationship, it is convenient for the realization of refutation process,It is taken as constant C, i.e.,Γ (t) characterizes the transverse field of a variation, to cause between different conditions Quantum is leaped.
Embodiment 2
Huainan Mining Area Guqiao Coal Mine 1414 (1) working face uses comprehensive mechanical coal mining, full-seam mining, whole caving methods Manage top plate, 343 days working face extraction time.Working face arranges that exploitation size is 2120m × 251m (work along bearing Face trend is sufficient mining, tendency subcritical extraction, generally subcritical extraction)), averagely mining height 3.0m, seam inclination are average It it is 5 °, working face is averaged buried depth 735m.The tendency line of observation is arranged in distance and cuts eye and stop adopting at line 1144m and 976m, lays 3 altogether A control point and 50 monitoring points, point spacing are 30m, and tendency line length is 1500m.It is inclined in direction of going down the hill to move towards line of observation setting At center of working face line distance 39m, 3 control points and 95 monitoring points are laid altogether, point spacing is 30m/60m, and length is 3480m。
Surface observation station proceeds by connection from October 19th, 2013 and measures, and on June 9th, 2015, (last time was seen Survey), observation work lasts about 20 months (totally 599 days), has carried out for the first time observation, 2 daily observation and 11 times comprehensively altogether comprehensively Observe the task in stages such as (last independently carry out twice).Care for 1414 (1) fully-mechanized mining working observation station of surface movement of bridge mine Plane translocation use D grades of GPS networks, comprehensive observation in mining active process, the requirement that plane survey is measured by GNSS CORS RTK It carries out；The measurement of higher degree waits geometrical standards method to carry out using four；Measurement and daily observation are maked an inspection tour, using four equal geometrical standards methods It carries out.
MPIPIMQA is utilized respectively to see with MPIPIMSA methods pair 1414 (1) working face observation station of surface movement Final Issue Measured data (surface movement enters stationary phase) carries out probability integral parametric inversion.In probability integration process parametric inversion, in order to keep away The contingency for exempting from inversion result is ensureing that initial parameter value and restriction range are identical, independently using each method Parametric inversion 10 times is carried out, and calculates error and the probability integral based on mean parameter in the average value of inverted parameters, parameter Method sinks, moves horizontally error in fitting, and experimental result is as shown in table 1.
Results contrasts of table 1 MPIPIMQA and MPIPIMSA in engineer application
It can be seen that from the experimental result of table 1：(1) in parameter in terms of error, with MPIPIMSA (based on simulated annealing Probability integral parameter inversion method) it compares, MPIPIMQA (the probability integral parameter inversion method based on Quantum annealing) inverting Probability integral parameter is in addition to part deviation of inflection point (Sd、Sl) in error it is bigger, other parameters (q, tan β, b, θ, Su) in error Respectively less than error in the parameter of MPIPIMSA illustrates that MPIPIMQA stability is better than MPIPIMSA.(2) the average ginseng of inverting is utilized Number, MPIPIMQA and MPIPIMSA fittings middle error of sinking and move horizontally are respectively 106.8863mm and 107.3220mm, because This MPIPIMQA fitting effect is better than MPIPIMSA, the sinking of two methods and to move horizontally fitting effect as shown in Figures 2 and 3, Wherein, the relevant parameter in Fig. 2 and Fig. 3 is described as follows：
Was, Uas-working face move towards actual measurement sinking and horizontal movement value；
Indicate MPIPIMQA invertings in two figures of QAs-respectively moves towards sinking and horizontal movement value；
Indicate MPIPIMSA invertings in two figures of SAs-respectively moves towards sinking and horizontal movement value；
Wai, Uai-working face tendency actual measurement sinking and horizontal movement value；
The tendency sinking and horizontal movement value of MPIPIMQA invertings are indicated in two figures of QAi-respectively；
The tendency sinking and horizontal movement value of MPIPIMSA invertings are indicated in two figures of SAi-respectively.
(3) seeking 1414 (1) working face probability integration process parameters (mean parameter) using MPIPIMQA is：Q=0.9916, Tan β=1.9277, b=0.4190, θ=84.3381, Su=-7.3715, Sd=-14.7126, Sl=59.0695, Sr= 32.6381。
Above-mentioned engineer application the result shows that, relative to the probability integral parameter inversion method based on simulated annealing, based on amount Son annealing probability integral parameter inversion method due to improve convergence rate, enhancing global optimization ability so that inverting it is general Rate integration method parameter is more accurate, more reliable.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously Cannot the limitation to the scope of the claims of the present invention therefore be interpreted as.It should be pointed out that for those of ordinary skill in the art For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.

Claims (1)

1. a kind of probability integration process parameter inversion method based on Quantum Annealing, which is characterized in that
Step 1 presets probability integral initial parameter value B0, temperature and transverse field change functionMost Low temperature and transverse field Tmin=Γmin, probability integral parameter fluctuation range ± Δ B, maximum allowable step-length scale and interior follow Ring number M；
Step 2, interior loop iteration start, and calculate the target function value f of initial parameter0=Σ ((Wa0-Wp0)2+(Ua0-Up0)2), lead to Cross the maximum allowable step-length scale and carry out calculating parameter value, set the object function of ith iteration as：fi=Σ ((Wpi-Wai )2+(Upi-Uai)2), the random number R and (j) between (0,1) is randomly generated, according to formula Bi+1(j)=Bi(j)+(2-Rand (j)) Δ B (j) calculates the parameter value B after each parameter i+1 time iterationi+1, whether judge the parameter after i+1 time iteration Within the scope of parameter fluctuation, if not existing, B is takeni+1(j)=Bi(j)；Otherwise, after the completion of iteration, the mesh of i+1 time iteration is calculated Scalar functions fi+1；
Step 3 calculates Δ f=fi+1-fiIf Δ f<0 or Δ f>0 andThen Select i+1 subparameter value Bi+1Instead of ith parameter value Bi；Otherwise, ith parameter value B is still selectedi；
Step 4 judges whether interior cycle-index m meets preset interior cycle-index M, if not satisfied, then entering step (2), it otherwise enters step (5)；
Step 5 judges whether to meet required precision, if satisfied, otherwise then stopping iteration output optimized parameter solution judges temperature T Whether within the allowable range with transverse field Г, if reducing temperature and transverse field, repeating step (2)-(4), otherwise jump out and follow Ring exports parametric solution at this time as optimized parameter solution.
Wherein, the probability integral parameter includes subsidence factor q, main influence angle tangent tan β, displacement factor b, maximum Lower sinking angle θ, upper deviation of inflection point Su, lower deviation of inflection point Sd, left deviation of inflection point SlWith right deviation of inflection point Sr, B0=[q, tan β,b,θ,Su,Sd,Sl,Sr], ± Δ B=[Δ q, Δ tan β, Δ b, Δ θ, Δ Su,ΔSd,ΔSl,ΔSr], t indicates iteration time Number, i=1:N, N are number of parameters, Wai、UaiThe sinking of terrene shift observing point actual measurement and horizontal shifting respectively above working face It is dynamic, Wpi、UpiRespectively ith iteration when observation point prediction sinking and horizontal movement value,H0 Indicate Hamilton amounts when ground state,Indicate i-th of particle in the spin Pauli presentation of x-axis, the cross of Γ (t) one variation of characterization To field, to cause the quantum between different conditions to be leaped.
CN201810255950.7A 2018-03-27 2018-03-27 A kind of probability integration process parameter inversion method based on Quantum Annealing Pending CN108491641A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810255950.7A CN108491641A (en) 2018-03-27 2018-03-27 A kind of probability integration process parameter inversion method based on Quantum Annealing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810255950.7A CN108491641A (en) 2018-03-27 2018-03-27 A kind of probability integration process parameter inversion method based on Quantum Annealing

Publications (1)

Publication Number Publication Date
CN108491641A true CN108491641A (en) 2018-09-04

Family

ID=63337455

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810255950.7A Pending CN108491641A (en) 2018-03-27 2018-03-27 A kind of probability integration process parameter inversion method based on Quantum Annealing

Country Status (1)

Country Link
CN (1) CN108491641A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110610017A (en) * 2019-07-16 2019-12-24 安徽理工大学 Mining subsidence prediction parameter solving method based on improved firework algorithm
CN111858229A (en) * 2019-04-26 2020-10-30 富士通株式会社 Optimization device and control method of optimization device
CN115097723A (en) * 2022-08-26 2022-09-23 南通启锦智能科技有限公司 PID control parameter adjusting method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1921752A1 (en) * 2006-11-13 2008-05-14 Global IP Solutions (GIPS) AB Adaptive arithmetic encoding and decoding of digital data
CN105531379A (en) * 2013-09-13 2016-04-27 科罗拉多州立大学董事会法人团体 Quantum molecular sequencing (qm-seq): identification of unique nanoelectronic tunneling spectroscopy fingerprints for dna, rna, and single nucleotide modifications
CN105806303A (en) * 2016-04-08 2016-07-27 安徽理工大学 Method for solving probability integral parameters by fusing D-InSAR and pattern search method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1921752A1 (en) * 2006-11-13 2008-05-14 Global IP Solutions (GIPS) AB Adaptive arithmetic encoding and decoding of digital data
CN105531379A (en) * 2013-09-13 2016-04-27 科罗拉多州立大学董事会法人团体 Quantum molecular sequencing (qm-seq): identification of unique nanoelectronic tunneling spectroscopy fingerprints for dna, rna, and single nucleotide modifications
CN105806303A (en) * 2016-04-08 2016-07-27 安徽理工大学 Method for solving probability integral parameters by fusing D-InSAR and pattern search method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
李培现: "开采影响地表点变形状态分析及主变形值计算", 《采矿与安全工程学报》 *
李楠等: "矿区地表沉陷的D-InSAＲ监测方法", 《金属矿山》 *
魏超: "量子退火反演的原理和实现", 《地球物理学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111858229A (en) * 2019-04-26 2020-10-30 富士通株式会社 Optimization device and control method of optimization device
CN110610017A (en) * 2019-07-16 2019-12-24 安徽理工大学 Mining subsidence prediction parameter solving method based on improved firework algorithm
CN110610017B (en) * 2019-07-16 2021-10-29 安徽理工大学 Mining subsidence prediction parameter solving method based on improved firework algorithm
CN115097723A (en) * 2022-08-26 2022-09-23 南通启锦智能科技有限公司 PID control parameter adjusting method
CN115097723B (en) * 2022-08-26 2022-11-22 南通启锦智能科技有限公司 PID control parameter adjusting method

Similar Documents

Publication Publication Date Title
CN108491641A (en) A kind of probability integration process parameter inversion method based on Quantum Annealing
Zhang et al. Parameterization of nearshore wave front slope
CN106644206B (en) A kind of pressure from surrounding rock calculation method for shallow tunnel
CN107145678B (en) Method for calibrating roughness of river channel plane two-dimensional water flow model
CN110889588A (en) Method for evaluating risk level of shield tunnel construction adjacent building by using factor judgment matrix
CN108763822A (en) A kind of accurate recognition methods of coal mine gob space geometry feature based on depression monitoring
CN106777742B (en) A kind of anchoring system design methods based on multiple groups part
CN107545115A (en) Submontane river bridge entirety scour forecast method under changing environment
CN107268662A (en) A kind of power transmission line iron tower foundation point hole method
Nielsen et al. Time-varying wave and current-induced scour around offshore wind turbines
CN114329950B (en) Dynamic generalization-based slope type submerged dike wave hydrodynamic influence numerical simulation method
CN106651054A (en) Method for identifying roughness of water conveyance channel of long-distance water transfer project based on genetic algorithm
CN110287504A (en) A kind of method for building up of the coupling model of simulation coastal seawater exchange rule
CN105447319A (en) Determination method for different geological interface calculation parameters of sliding blocks of arch dam abutment
CN104850918A (en) Node load prediction method taking power grid topology constraints into consideration
Dohm Pronounced minimum of the thermodynamic Casimir forces of O (n) symmetric film systems: Analytic theory
CN110543679A (en) multi-wave-condition large-size fixed marine platform horizontal force calculation method
CN102140786A (en) Manufacturing method of hydraulic model
CN112163382B (en) Hydrodynamic force simulation method for river network in plain polder area
CN105095649B (en) The data analysing method and system of building inclination angle
CN107704850B (en) A kind of Creek system landform identifying processing method
Ronda et al. Inter-annual wind speed variability on the North Sea
CN206616513U (en) Take into account the river model drainage system of bed surface stability and Quick drainage
Yu et al. Effects of a diversion dyke on river flow: a case study
CN103198367B (en) The detection method of reservoir bank slump prediction value

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20180904