CN108229076A - A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface - Google Patents
A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface Download PDFInfo
- Publication number
- CN108229076A CN108229076A CN201810146931.0A CN201810146931A CN108229076A CN 108229076 A CN108229076 A CN 108229076A CN 201810146931 A CN201810146931 A CN 201810146931A CN 108229076 A CN108229076 A CN 108229076A
- Authority
- CN
- China
- Prior art keywords
- parameter
- output characteristics
- ballast
- factor
- adjustment factor
- 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.)
- Granted
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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface, belongs to relay product design field.The invention aims to solve the problems, such as current Parameters design can not determine globally optimal solution, can not eliminate factor interactive effects scheme robustness, optimization precision it is low.Method is as follows:Determine controllable factor, error component and orthogonal test scheme;Signal-to-noise ratio, sensitivity significance analysis are carried out, determines ballast;Interacting property is analyzed, and determines adjustment factor;The response surface model of ballast and Robust Optimization object function are established, determines ballast optimal solution;Adjustment factor multinomial model and offset compensation object function are established, determines adjustment factor optimal solution.The present invention determines adjustment factor, adjustment factor is recycled to compensate the bias of output characteristics, adjusts output characteristics to desired value so as to fulfill in the case where the robustness for not influencing ballast is optimal by being decoupled to parameter.
Description
Technical field
The invention belongs to relay product design fields, and in particular to a kind of electromagnetic relay ginseng based on response surface
Number robustness global optimizing method.
Background technology
Parameter designing is the important link in electromagnetic relay product design process, not only directly determines the output of product
Can characteristic meet design requirement, but also directly affect product design scheme and externally interfere, interior interference, manufacture dispersibility etc.
The resistivity of uncertain factor, i.e. robustness.Therefore, electromagnetic relay product is improved using Robust Parameter Design method
Stability and quality conformance it is significant.
The key of Robust Parameter Design is, using the non-linear nature between input parameter and output characteristics, to realize
Do not control uncertainty, i.e., cost it is constant in the case of effectively improve the ability that designing scheme inhibits quality fluctuation.Traditional ginseng
When Calculation of Sensitivity result directly determines the noise that number design method is calculated using Orthogonal Experiment and Design and variance analysis
Go out parameter designing scheme, there are following two shortcomings for this method:It can only be chosen most from the discrete levels value that orthogonal test is chosen
It is excellent to combine scheme as an optimization, and globally optimal solution of the parameter in the range of solution can not be obtained;It can not ensure selected stabilization
The independence of factor and adjustment factor can influence to have determined that the robustness of scheme during output offset amount compensates.
Improvement Robust Parameter Design method based on Monte Carlo can improve the validity of statistical result and optimization knot
The accuracy of fruit.However, this method is improved only for the appearance in experimental design, the level value of interior watch test factor is still
It is so global optimizing that is discrete, therefore can not realizing parameter.
In addition, the traditional parameters design method based on approximate modeling fully enters parameter and output characteristics firstly the need of foundation
Between function model, so that optimal solution be calculated in domain.However there are following two shortcomings for this method:It is building
During vertical approximate model, if the input parameter number of levels chosen is less, it is difficult to ensure that model approaches actual function relationship well,
Influence optimization precision;And when input parameter is more, the number of coefficients to be asked during approximate modeling is more and calculating process is complicated,
Therefore the Parametric optimization problem of multiple input can not be suitable for inclusion in.
Invention content
Can not determine globally optimal solution the purpose of the present invention is to solve current electric equipment products Parameters design, can not
Elimination factor interactive effects scheme robustness, modeling process complexity etc. cause to optimize the problem of precision is low, provide one kind and are based on
The parameters of electromagnetic relay robustness global optimizing method of response surface.
To achieve the above object, the technical solution that the present invention takes is as follows:
A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface, the method includes following steps
Suddenly:
Step 1:Input parameter, uncertain factor are determined according to research object and optimization aim, appearance is orthogonal in progress
Experimental design;Wherein, interior table arranges input parameter, and appearance arranges uncertain factor, according to input parameter and uncertain factor
Quantity and number of levels select inside and outside orthogonal arrage respectively and determine testing program;
Step 2:The output characteristics of each testing program is calculated, external watch test result calculates signal-to-noise ratio and sensitivity, and defeated
Enter in interior table, then internal table carries out the variance analysis of signal-to-noise ratio and sensitivity, according to the significance analysis result of each input parameter
Determine its non-linear nature and approximately linear property;
Step 3:The correlation between input parameter is determined by Interaction Analysis, parameter is decoupled, in conjunction with letter
Ratio, sensitivity significance analysis result and Interaction Analysis make an uproar as a result, determining the ballast and adjustment factor in input parameter;
Step 4:Function model between ballast and output characteristics is established using Response surface meth od, meanwhile, to inhibit
Quality fluctuation is target, establishes Robust Optimization object function, then by global optimizing, will become output characteristics in domain
The parameter value of rate minimum is determined as the optimal solution of ballast;
Step 5:Polynomial function between adjustment factor and output characteristics is established using linear regression method, is counted simultaneously
The difference that output characteristics after ballast optimization deviates desired value is calculated, finally to compensate the offset of output characteristics as target,
Determine the value of adjustment factor.
The present invention is relative to the advantageous effect of the prior art:
(1) present invention can avoid the reciprocation between parameter to designing scheme robustness by the decoupling of input parameter
Influence, while modeling parameters quantity can be effectively reduced and improve modeling accuracy.
(2) it realizes global optimizing in the range of the solution that the method for the present invention can change in continuous parameters, ensures that robustness is set
Meter scheme it is optimal.
(3) the method for the present invention can carry out quantitative compensation in the case where not influencing scheme robustness to output offset amount,
Ensure that output characteristics meets design requirement.
(4) present invention determines adjustment factor, recycles adjustment factor to output characteristics by being decoupled to parameter
Bias compensates, and adjusts output characteristics to mesh so as to fulfill in the case where the robustness for not influencing ballast is optimal
Scale value.
Description of the drawings
Fig. 1 is the flow chart of the method for the invention.
Specific embodiment
Technical scheme of the present invention is further described with reference to embodiment, however, it is not limited to this, every right
Technical solution of the present invention is modified or equivalent replacement, without departing from the scope of technical solution of the present invention, should all cover
Among protection scope of the present invention.
Specific embodiment one:What present embodiment was recorded is a kind of parameters of electromagnetic relay robustness based on response surface
Global optimizing method, the described method comprises the following steps:
Step 1:Input parameter, uncertain factor are determined according to research object and optimization aim, appearance is orthogonal in progress
Experimental design;Wherein, interior table arranges input parameter, and appearance arranges uncertain factor, according to input parameter and uncertain factor
Quantity and number of levels selection orthogonal arrage and determine testing program;The research object is relay, and optimization aim is according to reality
Can be armature sucting speed, magnetic retentivity size etc. depending on the situation of border;Input parameter can be influence optimization aim after
Each vital part size of electric appliance or the relay coil number of turn;Uncertain factor, that is, noise factor can be production and processing
The actual size value of input parameter in process tolerance fluctuation range, i.e. A '=A ± tolerances, A are input parameter value, and A ' is not true
Determine factor;
Step 2:The output characteristics of each testing program is calculated, external watch test result calculates signal-to-noise ratio and sensitivity, and defeated
Enter in interior table, then internal table carries out the variance analysis of signal-to-noise ratio and sensitivity, according to the significance analysis result of each input parameter
Determine its non-linear nature and approximately linear property;
Corresponding orthogonal arrage is selected according to determining controllable input parameter quantity and uncertain factor quantity and number of levels, really
Determining scheme, (for example interior table determines 4 parameters, and each parameter has 3 number of levels, it is possible to using L9(34) orthogonal arrage, form 9
Kind scheme;Appearance also determines 4 parameters, and each parameter has 3 number of levels, it is possible to using L9(34) orthogonal arrage, form 9 kinds
Scheme;Every 1 interior table has 9 kinds of appearance schemes, just has 9 output characteristics as a result, total scheme is exactly 9 × 9=81 kinds);It is overall
Numerical procedure number=interior table scheme × appearance scheme.
Output characteristics can be selected according to object (relay), such as armature pickup time, armature sucting speed, magnetic holding
Size of power etc..
Step 3:The correlation between input parameter is determined by Interaction Analysis, parameter is decoupled, in conjunction with letter
Ratio, sensitivity significance analysis result and Interaction Analysis make an uproar as a result, determining the ballast and adjustment factor in input parameter;
Step 4:Function model between ballast and output characteristics is established using Response surface meth od, meanwhile, to inhibit
Quality fluctuation is target, establishes Robust Optimization object function, then by global optimizing, will become output characteristics in domain
The parameter value of rate minimum is determined as the optimal solution of ballast;
Step 5:Polynomial function between adjustment factor and output characteristics is established using linear regression method, is counted simultaneously
The difference that output characteristics after ballast optimization deviates desired value is calculated, finally to compensate the offset of output characteristics as target,
Determine the value of adjustment factor.
Specific embodiment two:A kind of parameters of electromagnetic relay based on response surface described in specific embodiment one is steady
Property global optimizing method, it is described that parameter is decoupled specially in step 3:Arbitrary 2 ginsengs are chosen from input parameter
It is several without recombination (X, Y), respectively first calculating parameter X and parameter Y independent changes when correspond to output characteristics variation delta x and
Then Δ y calculates the variation delta xy that output characteristics is corresponded to when (X, Y) combination changes simultaneously, if parameter X and parameter Y are complete
It is independent, then it should meet the mathematical relationship of Δ xy=Δ x+ Δs y, it is on the contrary then illustrate that there are reciprocations between parameter X and parameter Y;
Definition interaction factor gamma reflects interactive degree between parameter X and Y, at the same using following formula determine parameter it
Between reciprocation,
| Δ xy- (Δ x+ Δs y) | >=γ × | Δ xy |,
With reference to signal-to-noise ratio, sensitivity significance analysis result and Interaction Analysis as a result, will have approximately linear property and
It is determined as adjustment factor with the mutually independent input parameter of ballast.
Specific embodiment three:A kind of parameters of electromagnetic relay based on response surface described in specific embodiment one is steady
Property global optimizing method, in step 5, it is described established using linear regression method it is more between adjustment factor and output characteristics
Formula function is specially:After ballast prioritization scheme is determined, output characteristics can deviate with the change of parameter value,
The offset Δ F of output characteristics is calculated firsts, then Joint regulation is because of prime polynomial Fa, establish offset compensation target H2, such as
Shown in following formula:
It, can be right in the case where not influencing scheme robustness due to mutual indepedent between adjustment factor and ballast
Output offset amount carries out quantitative compensation, finally determining adjustment factor XaDesigning scheme.
Embodiment 1:
In step 1, input parameter and uncertain factor are determined according to research object and optimization aim, inside and outside progress
Table Orthogonal Experiment and Design.Wherein, it using input parameter as controllable factor, is arranged into interior table orthogonal arrage.By uncertain factor
As error component, it is arranged into appearance orthogonal arrage.Quantity further according to factor and number of levels select corresponding orthogonal arrage and true
Determine testing program.
In step 2, the corresponding testing program of every appearance (i) is calculated respectively, acquires output characteristics y, then
It substitutes into following formula and calculates signal-to-noise ratio SiWith sensitivity ηi:
In formula, m is outer watch test number;SmiAverage value for output characteristics fluctuates;VeiEstimated value for error variance;
DB is decibel value.Smi、VeiCalculation formula it is as follows:
In formula,Average value for the mass property under interior table i schemes;J is the corresponding appearance quantity of single interior table scheme;
yijThe corresponding output characteristics of j-th of appearance for table in i-th.
After signal-to-noise ratio and Calculation of Sensitivity result are inserted interior table, determine each input parameter to noise by variance analysis
Than the conspicuousness with sensitivity.Wherein, to SNR influence, significant factor has stronger non-linear nature, is determined as stablizing
Factor.It is not notable on SNR influence and significant factor is influenced on sensitivity there is stronger linear behavio(u)r, and with reference to step
Three Interaction Analysis result is determined as adjustment factor.
In step 3, taken from input parameter arbitrary 2 parameters without recombination (X, Y), respectively first calculating parameter X
The variation delta x of output characteristics and Δ y is corresponded to when changing respectively with parameter Y.Then it calculates when (X, Y) combination changes simultaneously and corresponds to
The variation delta xy of output characteristics.If parameter X is completely independent with parameter Y, the mathematics that should meet Δ xy=Δ x+ Δs y closes
System.It is on the contrary then illustrate that there are reciprocations between parameter X and parameter Y.
Definition interaction factor gamma reflects interactive degree between parameter X and Y, at the same using following formula determine parameter it
Between reciprocation,
|Δxy-(Δx+Δy)|≥γ×|Δxy|。
With reference to signal-to-noise ratio, sensitivity significance analysis result and Interaction Analysis as a result, will have approximately linear property and
It is determined as adjustment factor with the mutually independent input parameter of ballast.
In step 4, the function model between ballast and output characteristics is established using Response surface meth od.It is false first
Determine have following relationship between system output response and ballast experimental data point:
In formula:G (X) is the Response Face Function established;X is independent from each other basic random variables vector;xiFor input
I-th of experimental data point;b0、bi、biiRespectively correspond to constant term, first order, quadratic term undetermined coefficient.
Specific iterative step is as follows:
The first step, it is assumed that initial center point X(1)=(x1 (1),x2 (1)...xn (1))。xn (1)It is inputted for first time iterative process
Nth data point value.
Second step imports approximation function functionWithFormula
In (1) represent the 1st iterative process, obtain 2n+1 test estimated value.Wherein σiFor stochastic variable xiStandard deviation, f for repeatedly
Ride instead of walk length, as the case may be depending on.
Third walks, and using estimated value row solving equations, undetermined coefficient b values is solved, so as to obtain function at current iteration point
Approximate equation.
4th step calculates reliability index β(k)With limit state equation design points x*(k), wherein k refer to kth step iteration mistake
Journey.
5th step calculates | β(k)-β(k-1)|<ε, ε are given accuracy, if condition is unsatisfactory for, determine that k+1 steps change according to the following formula
For central point X(k+1), it returns to second step and carries out next iteration,
In formula, XkRepresent kth step iteration central value, g (x) is approximation function function, and * (k) represents kth time iteration in above formula
Process has enabled in literaryIf condition meets, iterative process terminates, you can establishes and rings
Answer surface model.
Then, quality fluctuation to be inhibited to establish Robust Optimization object function as target.By ballast multinomial FsPoint
It is other to each ballast XsLocal derviation is sought, obtains slope of the function on respective factor direction.Slope shows that function exists closer to 0
Stability herein is better.Therefore, object function F is established into the progress square summation of more than slope valuesta, so as to which robustness is excellent
Change problem H1It is attributed to and seeks object function FstaMinimum value, be shown below,
In formula, xsiRepresent i-th of ballast (Xs) point, XsFor ballast, value range XsminTo Xsmax, H1
For Robust Optimization object function
Using the global optimizing method of sequential quadratic programming, determine that ballast makes object function F in domainsta
The optimal case of minimum parameter combination, as ballast.
In step 5, the polynomial function between adjustment factor and output characteristics is established using linear regression method.
After determining ballast prioritization scheme, output characteristics can deviate with the change of parameter value.It is special that output is calculated first
The offset Δ F of propertys, then Joint regulation is because of prime polynomial Fa, establish offset compensation target H2, it is shown below.
In formula, XaIt is factor X in its fluctuation range XaminTo XamaxAn interior variable, △ XaFor parameter value deviate to
Determine the size of X.
It, can be right in the case where not influencing scheme robustness due to mutual indepedent between adjustment factor and ballast
Output offset amount carries out quantitative compensation, finally determining adjustment factor XaDesigning scheme.
Claims (3)
- A kind of 1. parameters of electromagnetic relay robustness global optimizing method based on response surface, it is characterised in that:The method packet Include following steps:Step 1:Input parameter, uncertain factor are determined according to research object and optimization aim, appearance orthogonal test in progress Design;Wherein, interior table arranges input parameter, and appearance arranges uncertain factor, according to the number of input parameter and uncertain factor Amount and number of levels select inside and outside orthogonal arrage and determine testing program respectively;Step 2:The output characteristics of each testing program is calculated, external watch test result calculates signal-to-noise ratio and sensitivity, and in input In table, then internal table carries out the variance analysis of signal-to-noise ratio and sensitivity, is determined according to the significance analysis result of each input parameter Its non-linear nature and approximately linear property;Step 3:The correlation between input parameter is determined by Interaction Analysis, parameter is decoupled, in conjunction with noise Than, sensitivity significance analysis result and Interaction Analysis as a result, determining the ballast and adjustment factor in input parameter;Step 4:Function model between ballast and output characteristics is established using Response surface meth od, meanwhile, to inhibit quality It fluctuates as target, establishes Robust Optimization object function, then by global optimizing, output characteristics change rate will be made in domain Minimum parameter value is determined as the optimal solution of ballast;Step 5:Polynomial function between adjustment factor and output characteristics is established using linear regression method, is calculated simultaneously Output characteristics deviates the difference of desired value after ballast optimization, finally using the offset for compensating output characteristics as target, determines The value of adjustment factor.
- 2. a kind of parameters of electromagnetic relay robustness global optimizing method based on response surface according to claim 1, It is characterized in that:It is described that parameter is decoupled specially in step 3:The nothing of arbitrary 2 parameters is chosen from input parameter The variation delta x of output characteristics and Δ y is corresponded to when recombination (X, Y), respectively first calculating parameter X and parameter Y independent changes, so The variation delta xy that output characteristics is corresponded to when (X, Y) combination changes simultaneously is calculated afterwards, if parameter X is completely independent with parameter Y, It should meet the mathematical relationship of Δ xy=Δ x+ Δs y, it is on the contrary then illustrate that there are reciprocations between parameter X and parameter Y;Definition interaction factor gamma reflects interactive degree between parameter X and Y, while is determined between parameter using following formula Reciprocation,| Δ xy- (Δ x+ Δs y) | >=γ × | Δ xy |,With reference to signal-to-noise ratio, sensitivity significance analysis result and Interaction Analysis as a result, will have approximately linear property and with it is steady Determine the mutually independent input parameter of factor and be determined as adjustment factor.
- 3. a kind of parameters of electromagnetic relay robustness global optimizing method based on response surface according to claim 1, It is characterized in that:In step 5, the multinomial letter established using linear regression method between adjustment factor and output characteristics Number is specially:After ballast prioritization scheme is determined, output characteristics can deviate with the change of parameter value, count first Calculate the offset Δ F of output characteristicss, then Joint regulation is because of prime polynomial Fa, establish offset compensation target H2, such as following formula institute Show:
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810146931.0A CN108229076B (en) | 2018-02-12 | 2018-02-12 | A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810146931.0A CN108229076B (en) | 2018-02-12 | 2018-02-12 | A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface |
Publications (2)
Publication Number | Publication Date |
---|---|
CN108229076A true CN108229076A (en) | 2018-06-29 |
CN108229076B CN108229076B (en) | 2019-04-30 |
Family
ID=62661700
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810146931.0A Active CN108229076B (en) | 2018-02-12 | 2018-02-12 | A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108229076B (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109255182A (en) * | 2018-09-09 | 2019-01-22 | 浙江工业大学 | A kind of hard brittle material technology-parameter predictive model and its Multipurpose Optimal Method |
CN110135090A (en) * | 2019-05-21 | 2019-08-16 | 北京航空航天大学 | A kind of modeling of circuit system tolerance and analysis method based on response phase method |
CN111027251A (en) * | 2019-12-11 | 2020-04-17 | 哈尔滨工业大学 | Preference set-based electromagnetic relay robustness optimization design method |
CN111046555A (en) * | 2019-12-11 | 2020-04-21 | 哈尔滨工业大学 | Time-varying degradation quality characteristic compensation full life cycle quality robustness optimization method |
CN115203860A (en) * | 2022-08-03 | 2022-10-18 | 哈尔滨工业大学 | Polarized relay tolerance automatic allocation method considering manufacturing cost |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007257380A (en) * | 2006-03-23 | 2007-10-04 | Toyota Central Res & Dev Lab Inc | Optimal parameter search method and program thereof |
CN101765258A (en) * | 2009-12-28 | 2010-06-30 | 东北大学 | Three-phase electrode positioning device in smelting process of electro-fused magnesia furnace and control method thereof |
CN105183997A (en) * | 2015-09-14 | 2015-12-23 | 哈尔滨工业大学 | Thermal conduction model calibrating method based on double-deck nesting uncertainty propagation |
CN107609234A (en) * | 2017-08-28 | 2018-01-19 | 西北工业大学 | Based on probabilistic Robustness Analysis method and system |
-
2018
- 2018-02-12 CN CN201810146931.0A patent/CN108229076B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007257380A (en) * | 2006-03-23 | 2007-10-04 | Toyota Central Res & Dev Lab Inc | Optimal parameter search method and program thereof |
CN101765258A (en) * | 2009-12-28 | 2010-06-30 | 东北大学 | Three-phase electrode positioning device in smelting process of electro-fused magnesia furnace and control method thereof |
CN105183997A (en) * | 2015-09-14 | 2015-12-23 | 哈尔滨工业大学 | Thermal conduction model calibrating method based on double-deck nesting uncertainty propagation |
CN107609234A (en) * | 2017-08-28 | 2018-01-19 | 西北工业大学 | Based on probabilistic Robustness Analysis method and system |
Non-Patent Citations (1)
Title |
---|
杨文英 等: "继电器电磁机构电磁-热耦合模型建立与计算方法", 《电工技术学报》 * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109255182A (en) * | 2018-09-09 | 2019-01-22 | 浙江工业大学 | A kind of hard brittle material technology-parameter predictive model and its Multipurpose Optimal Method |
CN110135090A (en) * | 2019-05-21 | 2019-08-16 | 北京航空航天大学 | A kind of modeling of circuit system tolerance and analysis method based on response phase method |
CN110135090B (en) * | 2019-05-21 | 2020-10-09 | 北京航空航天大学 | Circuit system tolerance modeling and analyzing method based on response surface method |
CN111027251A (en) * | 2019-12-11 | 2020-04-17 | 哈尔滨工业大学 | Preference set-based electromagnetic relay robustness optimization design method |
CN111046555A (en) * | 2019-12-11 | 2020-04-21 | 哈尔滨工业大学 | Time-varying degradation quality characteristic compensation full life cycle quality robustness optimization method |
CN111027251B (en) * | 2019-12-11 | 2021-06-01 | 哈尔滨工业大学 | Preference set-based electromagnetic relay robustness optimization design method |
CN111046555B (en) * | 2019-12-11 | 2022-04-08 | 哈尔滨工业大学 | Time-varying degradation quality characteristic compensation full life cycle quality robustness optimization method |
CN115203860A (en) * | 2022-08-03 | 2022-10-18 | 哈尔滨工业大学 | Polarized relay tolerance automatic allocation method considering manufacturing cost |
CN115203860B (en) * | 2022-08-03 | 2024-04-16 | 哈尔滨工业大学 | Polarization relay tolerance automatic distribution method considering manufacturing cost |
Also Published As
Publication number | Publication date |
---|---|
CN108229076B (en) | 2019-04-30 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108229076B (en) | A kind of parameters of electromagnetic relay robustness global optimizing method based on response surface | |
Zhang et al. | Variance‐constrained state estimation for networked multi‐rate systems with measurement quantization and probabilistic sensor failures | |
US9837991B2 (en) | Adaptive filter for system identification | |
CN109508510B (en) | Improved Kalman filtering-based rubidium atomic clock parameter estimation algorithm | |
Pineda et al. | Diffusing opinions in bounded confidence processes | |
KR19990071784A (en) | Monitoring and analysis system for manufacturing processes using single-step feedback and statistical simulation | |
Dong et al. | Extended dissipative sliding-mode control for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems | |
Ahn et al. | Dissipativity analysis for fixed-point interfered digital filters | |
Feng et al. | A stable adaptive implementation of the internal model principle | |
Mahdianfar et al. | Robust multiple model adaptive control: Modified using ν‐gap metric | |
CN108512528B (en) | Ratio control and normalization LMP filtering method under a kind of CIM function | |
CN111597753A (en) | Data depth change characteristic self-adaptive two-dimensional resistivity inversion method and system | |
CN108416148A (en) | A kind of high-altitude electromagnetic pulse field wire coupling uncertainty acquisition methods based on polynomial chaos expression | |
CN108256267A (en) | A kind of relay quality fluctuation based on radial basis function neural network inhibits design method | |
CN108268744A (en) | A kind of frame circuit breaker Robust Parameter Design method based on Kriging | |
Burnham et al. | Self‐tuning control of bilinear systems | |
CN108256268A (en) | A kind of relay parameter global optimizing method based on K-means radial basis function | |
Zhao et al. | Outlier-resistant l 2-l∞ state estimation for discrete-time memristive neural networks with time-delays | |
Khattak et al. | Leaky least mean fourth adaptive algorithm | |
CN108319794A (en) | A kind of electromagnetic relay quality conformance design method based on Orthogonal Least Squares | |
CN109388065A (en) | A kind of interference observer design method comprising closed loop reference model | |
CN106200619B (en) | The PI control loop performance estimating method of subsidiary controller output constraint | |
Togawa et al. | Advantages and challenges of non-intrusive polynomial chaos theory | |
CN114266349A (en) | Load flow calculation method based on adaptive neural network | |
Hanif et al. | On the convergence of a nash seeking algorithm with stochastic state dependent payoff |
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 | ||
GR01 | Patent grant | ||
GR01 | Patent grant |