CN107515992A - A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation - Google Patents
A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation Download PDFInfo
- Publication number
- CN107515992A CN107515992A CN201710797946.9A CN201710797946A CN107515992A CN 107515992 A CN107515992 A CN 107515992A CN 201710797946 A CN201710797946 A CN 201710797946A CN 107515992 A CN107515992 A CN 107515992A
- Authority
- CN
- China
- Prior art keywords
- mrow
- individual
- msubsup
- mtd
- random number
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Microelectronics & Electronic Packaging (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 JA Model Parameter Optimization self-adapted genetic algorithms in being emulated the present invention relates to ferromagnetic material hysteresis characteristic, specifically during JA Model Parameter Optimizations, using one group of JA model parameter as individual, individual includes 5 locus, five parameters of JA models are corresponded to respectively, and multigroup JA model parameters form population.Ideal adaptation ability degree of agreement of hysteresis curve and known hysteresis curve obtained by corresponding emulation determines.Appropriate genetic manipulation is carried out to population, until obtaining preferably one group of parameter.The beneficial effects of the invention are as follows：1st, there is positive effect to the parameter optimization for emulating hysteresis curve；2nd, hysteresis curve and original hysteresis curve are very close corresponding to the optimum individual obtained to actual silicon steel sheet hysteresis curve parameter optimization, and effect of optimization is obvious.
Description
Technical field
The invention belongs to electrical engineering technical field, and in particular to the JA models in a kind of ferromagnetic material hysteresis characteristic emulation
Parameter optimization self-adapted genetic algorithm.
Background technology
The DC current of Transformer Winding produces DC magnetizing strength in iron circuit, due to the magnetic hysteresis of transformer core
Characteristic, DC magnetizing strength influence each other with ac magnetization intensity, cause ac magnetization intensity to change, and produce D.C. magnetic biasing
Phenomenon.Hysteresis characteristic research unshakable in one's determination is the key point of transformer DC magnetic bias research.Due to the theoretical developments of current magnetics
Still incomplete, interpretation model to hysteresis is simultaneously few, and JA models are a widely used kinds.JA model use magnetic
The concept of domain wall by magnetic domain magnetic history resolve into friction effect can not inverse component Mirr and elastic reversible component Mrev, profit
The relation between magnetization M and magnetic field intensity H is described with the Langevin function of modification, and finally obtains magnetic induction density B and H
Between relation.JA models can preferably describe hysteresis, but its model is complex, by the difference of 5 parameters
Value distinguishes the different hysteresis characteristics that different materials have.In the application of JA models, the accurate of 5 parameters included in model takes
Value is key point, and more difficult.On the other hand, domestic and foreign scholars propose a variety of optimized algorithms, but exist in terms of two
Problem, when it is incorrect to the metric form of effect of optimization, second, these algorithms are mostly to be directed to hard magnetic material and ultra-magnetic telescopic
Material, it is rare to be directed to soft magnetic materials as transformer silicon steel sheet.
Under the conditions of certain known ferromagnetic material hysteresis curve, it must be determined that the parameter of suitable JA models, count emulation
Obtained hysteresis curve coincide with known hysteresis curve.The problem of asking for of JA model parameters is actually in 5 dimension spaces
Nonlinear optimal problem, the present invention proposes a kind of JA Model Parameter Optimizations self-adapted genetic algorithm to solve this problem.
The content of the invention
The technical problem to be solved in the present invention is, for the different hysteresis curves of same ferromagnetic material, there is provided one kind is applied to
It is determined that the optimization self-adapted genetic algorithm of corresponding JA model parameters.
The technical solution of the present invention is, during JA Model Parameter Optimizations, using one group of JA model parameter as individual
Body, individual include 5 locus, correspond to five parameters of JA models respectively, and multigroup JA model parameters form population.Individual is suitable
Should be able to power determined by the degree of agreement of hysteresis curve and known hysteresis curve obtained by corresponding emulation.Appropriate something lost is carried out to population
Operation is passed, until obtaining preferably one group of parameter.
Specifically：
A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation, it is characterised in that
Comprise the following steps：
Step 1, N number of point equidistant using on hysteresis curve pass through computer sim- ulation hysteresis curve as " characteristic point " of loop line
Character pair point describes degree of agreement apart from sum between known hysteresis curve, and as the evaluation function of degree of agreement；
Step 2, the parameters using real number representation JA models；It is random between 0~1 by producing in view of computer
Count to create each individual of initial population, and for ease of intersecting the design with mutation operator, to the coding range of each parameter
It is set as 0~1, i.e., using Normalization real number encoding method；
Step 3, adoption rate are selected as selection strategy, and specific implementation procedure is：It is all individual in colony at calculating
Adaptive value summationAs wheel disc size；With each individual adaptive value f (Xi) individual is used as on wheel disc
The size in region is occupied, the region that such m-th of individual occupies on wheel disc isWith
Computer produces a random number δ, if δ × F fallsIn section, then m-th of individual quilt
Participation is chosen to follow-on heredity；
Step 4, the crossover operator being combined using arithmetic crossover with multiple-spot detection；
Step 5, for JA Model Parameter Optimization features, it is proposed that a kind of adaptive non-uniform mutation of variation amplitude is calculated
Son；
Step 6, the population to individual amount for P, before carrying out genetic manipulation per a generation, first preserve optimum individual Ibest
Get up；Then by selecting, intersecting and mutation operation, P-2 individual is obtained；Again by the optimum individual Ibest preserved and
The new individual Inew randomly generated is added, and so forms the population of new generation that individual amount is P；
Step 7, adaptive value f (X are calculated to the individual randomly generatedi), if adaptive value is less than given threshold Δ f, abandon
This individual, then an individual is randomly generated, until P adaptive value of generation is both greater than Δ f individual composition initial population.
JA Model Parameter Optimization self-adapted genetic algorithms in a kind of above-mentioned ferromagnetic material hysteresis characteristic emulation, it is described
Step 4 specifically includes following sub-step：
Step 2.1, by selection obtain two parental generation individuals after, computer produce a random number RIIf less than individual
Crossover probability threshold value Δ RI, then arithmetic crossover operation is made as the following formula to the two individuals；Otherwise enter in next step；
For filial generation,For parental generation, rand is random number；
Step 2.2,5 locus for individual, it is corresponding to produce 5 random number (R1~R5), if caused random number is small
In gene crossover probability threshold value Δ Rg, then correspond to locus and make arithmetic crossover, otherwise correspond to locus and keep constant.
JA Model Parameter Optimization self-adapted genetic algorithms in a kind of above-mentioned ferromagnetic material hysteresis characteristic emulation, it is described
Step 5 specifically includes：For a certain individual, a random number R is producedmIf RmLess than the individual variation threshold value Δ R of settingm, then
5 random numbers are produced to 5 genes of the individual, if caused random number is less than genetic mutation threshold value, then produce random number
Rand, and it is based on current genetic algebra g and maximum genetic algebra gmNon-uniform mutation is made to the Gene A：
Caused new gene value A' is vibrated centered on A after being made a variation by above formula to Gene A, and the amplitude of vibration is with current heredity
Algebraically g change, algorithm early stage, General Oscillation amplitude is big, be easy to strengthen ability of searching optimum, algorithm later stage individual mostly compared with
Excellent, adaptive value is larger, and now Oscillation Amplitude reduces, and is easy to search for more excellent solution near more excellent solution.
The technological core of the present invention be during JA Model Parameter Optimizations, it is individual using one group of JA model parameter as individual
Body includes 5 locus, corresponds to five parameters of JA models respectively, and multigroup JA model parameters form population.Ideal adaptation energy
Power degree of agreement of hysteresis curve and known hysteresis curve obtained by corresponding emulation determines.Appropriate heredity is carried out to population to grasp
Make, until obtaining preferably one group of parameter.
The beneficial effects of the invention are as follows：(1), there is positive effect (2), to actual silicon to the parameter optimization for emulating hysteresis curve
Hysteresis curve and original hysteresis curve are very close corresponding to the optimum individual that steel disc hysteresis curve parameter optimization obtains, optimization effect
Fruit is obvious.
Brief description of the drawings
Fig. 1 is the workflow reference chart of the present invention.
Embodiment
Below by embodiment, and with reference to accompanying drawing, technical scheme is described in further detail.
Embodiment：
1st, evaluation function：In the optimization of JA model parameters, the quality of one group of parameter is decided by magnetic corresponding to this group of parameter
" identical " degree of hysteresis curves and known hysteresis curve, it is in the calculation using " identical " degree, it is necessary to quantify to it.Two
Group hysteresis curve " identical " is better, it is meant that " distance " is smaller between them, and based on this, the present invention proposes first-class with hysteresis curve
Away from " characteristic point " of N number of point as loop line, by character pair point between computer sim- ulation hysteresis curve and known hysteresis curve away from
Degree of agreement is described from sum, and as the evaluation function of degree of agreement.
2nd, coding method:Each excursion is all very big for 5 parameters of JA models, and difference is very between each parameter
Greatly, and often the small change of a parameter will cause the acute variation of hysteresis curve shape, therefore to the precision of each parameter
It is very high, in consideration of it, parameters of the present invention research using real number representation JA models.In view of computer by produce 0~1 it
Between random number create each individual of initial population, and for ease of intersecting and the design of mutation operator, to each parameter
Coding range is set as 0~1, i.e., using Normalization real number encoding method.
3rd, selection strategy：The selection strategy of the present invention selects for ratio, and specific implementation procedure is：Own at calculating in colony
The adaptive value summation of individualAs wheel disc size；With each individual adaptive value f (Xi) exist as the individual
The size in region is occupied on wheel disc, the region that such m-th of individual occupies on wheel disc is
A random number δ is produced with computer, if δ × F fallsIn section, then m-th individual
It is selected to participate in follow-on heredity.
4th, crossover operator：The crossover operator that the present invention is combined using arithmetic crossover with multiple-spot detection, specific implementation strategy
It is as follows：
(1) after obtaining two parental generation individuals by selection, computer produces a random number RIIf general less than individual intersection
Rate threshold value Δ RI, then arithmetic crossover operation is made as the following formula to the two individuals；Otherwise enter in next step；
For filial generation,For parental generation, rand is random number.
(2) it is corresponding to produce 5 random number (R for 5 locus of individual1~R5), if caused random number is less than base
Because of crossover probability threshold value Δ Rg, then correspond to locus and make arithmetic crossover, otherwise correspond to locus and keep constant.
Individual intersection probability Δ R in calculationIValue 0.7, gene crossover probability Δ RgValue 0.3.
5th, mutation operator：The present invention be directed to JA Model Parameter Optimization features, it is proposed that a kind of variation amplitude adaptively it is non-
Uniform mutation operator, implementation method are as follows：
For a certain individual, a random number R is producedmIf RmLess than the individual variation threshold value Δ R of settingm, then to this
5 genes of body produce 5 random numbers, if caused random number is less than genetic mutation threshold value, then produce random number rand, and
Based on current genetic algebra g and maximum genetic algebra gmNon-uniform mutation is made to the Gene A：
Caused new gene value A' is vibrated centered on A after being made a variation by above formula to Gene A, and the amplitude of vibration is with current heredity
Algebraically g change, algorithm early stage, General Oscillation amplitude is big, be easy to strengthen ability of searching optimum, algorithm later stage individual mostly compared with
Excellent, adaptive value is larger, and now Oscillation Amplitude reduces, and is easy to search for more excellent solution near more excellent solution.
6th, optimum individual retains and external individual invasion is tactful：The present invention proposes that a kind of optimum individual retains and external individual
The strategy of invasion, specific execution method are：To the population that individual amount is P, before carrying out genetic manipulation per a generation, first by optimal
Body IbestSave；Then by selecting, intersecting and mutation operation, P-2 individual is obtained；Optimal will preserved again
Body IbestWith the new individual I randomly generatednewAdd, so form the population of new generation that individual amount is P.On the one hand protect
Card population optimum individual adaptive value " is not fallen back ", on the other hand ensures the diversity of population gene.
7th, the selection of initial population：The present invention calculates adaptive value f (X to the individual randomly generatedi), set if adaptive value is less than
Determine threshold value Δ f, then abandon this individual, then randomly generate an individual, until P adaptive value of generation is both greater than Δ f group of individuals
Into initial population.
Specific embodiment described herein is only to spirit explanation for example of the invention.Technology belonging to the present invention is led
The technical staff in domain can be made various modifications or supplement to described specific embodiment or be replaced using similar mode
Generation, but without departing from the spiritual of the present invention or surmount scope defined in appended claims.
Claims (3)
- A kind of 1. JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation, it is characterised in that bag Include following steps：Step 1, N number of point equidistant using on hysteresis curve are used as " characteristic point " of loop line, by computer sim- ulation hysteresis curve and Character pair point describes degree of agreement apart from sum between knowing hysteresis curve, and as the evaluation function of degree of agreement；Step 2, the parameters using real number representation JA models；In view of computer by produce 0~1 between random number come Each individual of initial population is created, and for ease of intersecting the design with mutation operator, the coding range of each parameter is all provided with It is set to 0~1, i.e., using Normalization real number encoding method；Step 3, adoption rate are selected as selection strategy, and specific implementation procedure is：All individual adaptations in colony at calculating It is worth summationAs wheel disc size；With each individual adaptive value f (Xi) occupied as the individual on wheel disc The size in region, the region that such m-th of individual occupies on wheel disc areWith calculating Machine produces a random number δ, if δ × F fallsIn section, then m-th of individual is selected Participate in follow-on heredity；Step 4, the crossover operator being combined using arithmetic crossover with multiple-spot detection；Step 5, for JA Model Parameter Optimization features, it is proposed that a kind of adaptive inhomogeneous boundary layer of variation amplitude；Step 6, the population to individual amount for P, before carrying out genetic manipulation per a generation, first save optimum individual Ibest； Then by selecting, intersecting and mutation operation, P-2 individual is obtained；Again by the optimum individual Ibest preserved and random production A raw new individual Inew is added, and so forms the population of new generation that individual amount is P；Step 7, adaptive value f (X are calculated to the individual randomly generatedi), if adaptive value is less than given threshold Δ f, abandon this Body, then an individual is randomly generated, until P adaptive value of generation is both greater than Δ f individual composition initial population.
- 2. the JA Model Parameter Optimization Adaptive Genetics in a kind of emulation of ferromagnetic material hysteresis characteristic according to claim Algorithm, it is characterised in that the step 4 specifically includes following sub-step：Step 2.1, by selection obtain two parental generation individuals after, computer produce a random number RIIf general less than individual intersection Rate threshold value Δ RI, then arithmetic crossover operation is made as the following formula to the two individuals；Otherwise enter in next step；<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>X</mi> <mi>i</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mo>=</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&CenterDot;</mo> <msubsup> <mi>X</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <msubsup> <mi>X</mi> <mi>j</mi> <mi>t</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>X</mi> <mi>j</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mo>=</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&CenterDot;</mo> <msubsup> <mi>X</mi> <mi>j</mi> <mi>t</mi> </msubsup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <msubsup> <mi>X</mi> <mi>i</mi> <mi>t</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced>For filial generation,For parental generation, rand is random number；Step 2.2,5 locus for individual, it is corresponding to produce 5 random number (R1~R5), if caused random number is less than base Because of crossover probability threshold value Δ Rg, then correspond to locus and make arithmetic crossover, otherwise correspond to locus and keep constant.
- 3. the JA Model Parameter Optimization Adaptive Genetics in a kind of emulation of ferromagnetic material hysteresis characteristic according to claim Algorithm, it is characterised in that the step 5 specifically includes：For a certain individual, a random number R is producedmIf RmLess than setting Individual variation threshold value Δ Rm, then 5 random numbers are produced to 5 genes of the individual, if caused random number is less than genetic mutation Threshold value, then random number rand is produced again, and be based on current genetic algebra g and maximum genetic algebra gmThe Gene A is made non-homogeneous Variation：<mrow> <msup> <mi>A</mi> <mo>&prime;</mo> </msup> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>A</mi> <mo>+</mo> <mn>2</mn> <mrow> <mo>(</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>-</mo> <mn>0.5</mn> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mi>g</mi> <msub> <mi>g</mi> <mi>m</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&GreaterEqual;</mo> <mn>0.5</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>A</mi> <mo>+</mo> <mn>2</mn> <mrow> <mo>(</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>-</mo> <mn>0.5</mn> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <mi>A</mi> <mo>&times;</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mi>g</mi> <msub> <mi>g</mi> <mi>m</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo><</mo> <mn>0.5</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>Caused new gene value A' is vibrated centered on A after being made a variation by above formula to Gene A, and the amplitude of vibration is with current genetic algebra G changes, and in algorithm early stage, General Oscillation amplitude is big, is easy to strengthen ability of searching optimum, mostly more excellent in algorithm later stage individual, Adaptive value is larger, and now Oscillation Amplitude reduces, and is easy to search for more excellent solution near more excellent solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710797946.9A CN107515992A (en) | 2017-09-06 | 2017-09-06 | A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710797946.9A CN107515992A (en) | 2017-09-06 | 2017-09-06 | A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107515992A true CN107515992A (en) | 2017-12-26 |
Family
ID=60725049
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710797946.9A Pending CN107515992A (en) | 2017-09-06 | 2017-09-06 | A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107515992A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034357A (en) * | 2018-06-08 | 2018-12-18 | 北京应用物理与计算数学研究所 | The method and device scanned for based on magnetic structure of the genetic algorithm to material |
| CN109086561A (en) * | 2018-10-08 | 2018-12-25 | 重庆邮电大学 | A kind of meter and anisotropic JA hysteresis model parameter extracting method |
| CN113255189A (en) * | 2021-06-03 | 2021-08-13 | 福州大学 | Multi-field coupling electromagnetic simulation method for high-speed switch valve electromagnet optimization |
| CN117272683A (en) * | 2023-11-13 | 2023-12-22 | 江苏南方永磁科技有限公司 | Model parameter optimization method and system for magnetic hysteresis effect simulation of ferromagnetic material |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1995025865A1 (en) * | 1994-03-22 | 1995-09-28 | Tekton | Seismic brake |
| CN102903007A (en) * | 2012-09-20 | 2013-01-30 | 西安科技大学 | Method for optimizing disaggregated model by adopting genetic algorithm |
| CN102916421A (en) * | 2012-10-22 | 2013-02-06 | 深圳供电局有限公司 | Transformer protection mechanism |
-
2017
- 2017-09-06 CN CN201710797946.9A patent/CN107515992A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1995025865A1 (en) * | 1994-03-22 | 1995-09-28 | Tekton | Seismic brake |
| CN102903007A (en) * | 2012-09-20 | 2013-01-30 | 西安科技大学 | Method for optimizing disaggregated model by adopting genetic algorithm |
| CN102916421A (en) * | 2012-10-22 | 2013-02-06 | 深圳供电局有限公司 | Transformer protection mechanism |
Non-Patent Citations (1)
| Title |
|---|
| 鲁海亮等: "A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops", 《JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034357A (en) * | 2018-06-08 | 2018-12-18 | 北京应用物理与计算数学研究所 | The method and device scanned for based on magnetic structure of the genetic algorithm to material |
| CN109034357B (en) * | 2018-06-08 | 2019-06-18 | 北京应用物理与计算数学研究所 | The method and device scanned for based on magnetic structure of the genetic algorithm to material |
| CN109086561A (en) * | 2018-10-08 | 2018-12-25 | 重庆邮电大学 | A kind of meter and anisotropic JA hysteresis model parameter extracting method |
| CN113255189A (en) * | 2021-06-03 | 2021-08-13 | 福州大学 | Multi-field coupling electromagnetic simulation method for high-speed switch valve electromagnet optimization |
| CN113255189B (en) * | 2021-06-03 | 2022-05-10 | 福州大学 | Multi-field coupling electromagnetic simulation method for optimizing high-speed switch valve electromagnet |
| CN117272683A (en) * | 2023-11-13 | 2023-12-22 | 江苏南方永磁科技有限公司 | Model parameter optimization method and system for magnetic hysteresis effect simulation of ferromagnetic material |
| CN117272683B (en) * | 2023-11-13 | 2024-01-26 | 江苏南方永磁科技有限公司 | Model parameter optimization method and system for magnetic hysteresis effect simulation of ferromagnetic material |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107515992A (en) | A kind of JA Model Parameter Optimization self-adapted genetic algorithms in ferromagnetic material hysteresis characteristic emulation | |
| CN110399677B (en) | Transformer direct-current magnetic bias simulation method based on J-A formula improved under magnetic bias state | |
| CN106372725A (en) | Chaotic quantum genetic algorithm-based stochastic resonance weak signal detection method | |
| Sasaki et al. | Topology optimization of IPM motor with aid of deep learning | |
| CN106786524A (en) | Load model parameters discrimination method based on noise-like signal and improved differential evolution | |
| Golden et al. | No unitary bootstrap for the fractal Ising model | |
| Zhang et al. | A novel hysteresis core loss model for magnetic laminations | |
| CN107292381A (en) | A kind of method that mixed biologic symbiosis for single object optimization is searched for | |
| CN107871024B (en) | Electromagnetic optimization method and device for high-temperature superconductive annular energy storage magnet | |
| CN107223165A (en) | Non-oriented electromagnetic steel sheet and preparation method thereof | |
| Bouaziz et al. | Crucial role of Fe in determining the hard magnetic properties of Nd 2 Fe 14 B | |
| CN105845427B (en) | Cross section of transformer core design method based on particle group optimizing | |
| Zou | Parameter estimation of extended Jiles–Atherton hysteresis model based on ISFLA | |
| Niu et al. | The electronic effect in the⟨ 100⟩ edge dislocation core system with a carbon atom in α-iron: a first-principles study | |
| CN113962094B (en) | High-frequency transformer optimization design method comprehensively considering vibration noise | |
| CN103105775A (en) | Layering iterative optimization scheduling method based on order optimization and online core limitation learning machine | |
| CN102323983B (en) | Method for automatically providing optimized synthetic jet parameters | |
| CN108053250A (en) | A kind of transformer Price optimization method and device based on genetic algorithm | |
| Pei et al. | Image segmentation based on differential evolution algorithm | |
| Woehrnschimmel et al. | Simulating the change of magnetic properties of electrical steel sheets due to punching | |
| Antkowiak et al. | Non-uniform coupling model of the frustrated chromium-based ring Cr8Ni | |
| CN114282422B (en) | Iron loss calculation method based on local hysteresis loop model | |
| Reljić et al. | The estimation of iron losses in a non-oriented electrical steel sheet based on the artificial neural network and the genetic algorithm approaches | |
| CN103150360B (en) | A kind of social networks layered approach based on domination set innovatory algorithm | |
| Jaindl et al. | Multi-objective optimization using evolution strategies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20171226 |
|
| WD01 | Invention patent application deemed withdrawn after publication |