CN111859627B - Parameter optimization method and device for component model - Google Patents
Parameter optimization method and device for component model Download PDFInfo
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Abstract
The invention provides a parameter optimization method of a component model, which comprises the following steps: obtaining actual measurement data of the component in an actual test environment; extracting parameters from the component model as genes, and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values; determining a target individual by utilizing a genetic algorithm based on the initial population to obtain a gene value of the target individual as an optimized parameter of the component model; the fitness of the individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment. The invention also provides a parameter optimization device of the component model, and the scheme of the invention can effectively solve the problem that the parameters of the component model are difficult to optimize in the prior art, and improve the accuracy of the simulation result.
Description
Technical Field
The invention relates to the field of simulation, in particular to a parameter optimization method and device for a component model.
Background
Electromagnetic compatibility (Electro Magnetic Compatibility, EMC) simulation is a forward electromagnetic compatibility design method based on software analysis, so that engineers can simulate radiation emission of circuits and components, determine whether the emission meets common EMC standards, avoid unnecessary design and achieve the purposes of saving time, cost and the like. The complex components used as main interference sources in EMC simulation often have the characteristics of complex modeling, large influence range and the like.
The current component model modeling mode is based on mathematical modeling and assisted by a spice language mode, and model parameter sources are mainly obtained from the component parameters given by official documents. However, for privacy purposes, the published parameters often deviate slightly from the actual measured parameters, so the modeled component models are often unsatisfactory.
Therefore, how to optimize the parameters of the component model has very important significance for the accuracy of the simulation test.
Disclosure of Invention
The present invention is directed to solving the problems associated with the prior art described above. The invention provides a parameter optimization method of a component model on one hand and a parameter optimization device of the component model on the other hand. By the scheme provided by the invention, the problem that the parameters of the component model are difficult to optimize in the prior art can be effectively solved, and the accuracy of the simulation result is improved.
The first aspect of the present invention provides a method for optimizing parameters of a component model, including: obtaining actual measurement data of the component in an actual test environment;
extracting parameters from the component model as genes, and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values;
determining a target individual based on the initial population by utilizing a genetic algorithm, wherein the gene value of the target individual is the optimized parameter of the component model;
the fitness of the individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment.
According to an embodiment of the present invention, the measured data and the simulation data each include time and a characteristic parameter corresponding to the time; the characteristic parameter comprises a voltage value and/or a current value.
According to one embodiment of the invention, the fitness of a single individual is based on fitness at a plurality of times, the fitness at a single time satisfying: q (d) =k1·a1+k2·a2;
wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
In accordance with one embodiment of the present invention,k1＞10k2；
wherein K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d;
where a (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d.
According to one embodiment of the invention, the determining the target individual based on the initial population and using a genetic algorithm comprises:
the following steps are circularly executed: performing simulation test on individuals in the initial population under the simulation test environment to obtain simulation data of each individual, determining fitness of the individual based on the actually measured data and the simulation data of the individual, selecting parents from the initial population according to the fitness of the individual, and generating new individuals based on the parents and genetic operators to update the initial population;
in the cycle, if an end condition is satisfied, selecting the individual with the highest fitness from the initial population as the target individual.
The second aspect of the present invention provides a parameter optimization apparatus for a component model, including:
the actual measurement data acquisition module: the method comprises the steps of obtaining actual measurement data of the component in an actual test environment;
an initialization module: the method comprises the steps of extracting parameters from the component model as genes, and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values;
and an optimization module: the genetic algorithm is used for determining a target individual based on the initial population, and the gene value of the target individual is the optimized parameter of the component model;
the fitness of the individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment.
According to an embodiment of the present invention, the measured data and the simulation data each include time and a characteristic parameter corresponding to the time; the characteristic parameter comprises a voltage value and/or a current value.
According to one embodiment of the invention, the fitness of an individual is based on fitness at a plurality of times, the fitness at a single time satisfying: q (d) =k1·a1+k2·a2;
wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
In accordance with one embodiment of the present invention,k1＞10k2；
wherein K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d;
where a (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d.
According to one embodiment of the invention, the optimization module comprises:
the simulation test sub-module is used for performing simulation test on the individuals in the initial population under the simulation test environment to obtain simulation data of the individuals;
the fitness determination submodule is used for determining fitness of an individual based on the actual measurement data and the simulation data of the individual;
the parent selection submodule is used for selecting parents from the initial population according to the fitness of individuals;
a population updating sub-module for generating a new population based on the parents and genetic operators to update the initial population;
and the judging sub-module is used for selecting the individual with the highest fitness from the initial population as the target individual when the ending condition is met.
A third aspect of the present invention provides an electronic device for optimizing parameters of a component model, including: a memory for storing computer instructions; and a processor for reading and executing the computer instructions from the memory, thereby implementing the parameter optimization method provided by the first aspect of the present invention or an embodiment thereof.
A fourth aspect of the present invention provides a device simulation apparatus, comprising:
the modeling module is used for generating a component model and/or supporting the component model;
the second aspect of the present invention or embodiments thereof provide a parameter optimization apparatus;
and the simulation module is used for simulating based on the optimized component model.
With the embodiments of the present invention, the invention has the following effects: the accuracy of the simulation model is improved, so that the simulation model is more in line with the actual electrical characteristics of the device; the working efficiency of a designer is improved, repeated labor is reduced, and the modeling period of fitting of devices is quickened.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for optimizing parameters of a component model according to an exemplary embodiment of the present invention.
Fig. 2 is a flow chart of a method for optimizing parameters of a component model according to an exemplary embodiment of the present invention.
FIG. 3 is a flow chart of optimizing parameters of a component model using a genetic algorithm in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a schematic diagram of a parameter optimization apparatus of a component model according to an exemplary embodiment of the present invention;
FIG. 5 is a schematic diagram of an electronic device according to an exemplary embodiment of the invention;
fig. 6 is a schematic diagram of a component simulation apparatus according to an exemplary embodiment of the present invention.
Detailed Description
As used herein, the terms "first," "second," and the like may be used to describe elements in exemplary embodiments of the present invention. These terms are only used to distinguish one element from another element, and the inherent feature or sequence of the corresponding element, etc. is not limited by the terms. Unless defined otherwise, all terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Those skilled in the art will understand that the devices and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the present invention is defined solely by the claims. The features illustrated or described in connection with one exemplary embodiment may be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention.
Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, detailed descriptions of related known functions or configurations are omitted so as not to unnecessarily obscure the technical gist of the present invention. In addition, throughout the description, the same reference numerals denote the same circuits, modules or units, and repetitive descriptions of the same circuits, modules or units are omitted for brevity.
Furthermore, it should be understood that one or more of the following methods or aspects thereof may be performed by at least one control unit or controller. The term "control unit" or "controller" may refer to a hardware device comprising a memory and a processor. The memory is configured to store program instructions, and the processor is specifically configured to execute the program instructions to perform one or more processes that will be described further below. Moreover, it should be appreciated that the following methods may be performed by an apparatus comprising a control unit in combination with one or more other components, as will be appreciated by one of ordinary skill in the art.
Fig. 1 is a flow chart of a method for optimizing parameters of a component model according to an exemplary embodiment of the present invention. As shown in fig. 1, according to an exemplary embodiment of the present invention, a parameter optimization method of a component model includes:
100: and obtaining actual measurement data of the component in an actual test environment.
102: and extracting parameters from the component model as genes, and establishing an initial population. Individuals in the initial population have randomly assigned gene values.
104: and determining a target individual based on the initial population by utilizing a genetic algorithm, wherein the gene value of the target individual is the optimized parameter of the component model. The fitness of the individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment.
According to the analysis of the inventor, the existing component modeling has the following problems: manufacturers cannot provide accurate device parameters due to technical confidentiality, and technicians need to optimize the parameters; in the process of parameter optimization, the problems of large parameter quantity, undefined parameter influence and the like exist, and technicians usually have the problems of complex logic, complex operation, high time cost and the like in the current adjustment process; the effectiveness of the optimization results is difficult to judge.
By adopting the parameter optimization method provided by the embodiment, the optimization direction of the components is guided by using the measured data, so that the effectiveness of the optimization result is ensured; the genetic algorithm is used for carrying out unified processing on the parameters, on one hand, the optimal solution of the parameter output result can be achieved through moderate iteration, and approximation fitting is not needed like other methods; on the other hand, the optimization is performed by negating the wrong individual, and the optimization is not forced to be completely consistent with the actual measurement result. Therefore, the problems are solved, and the parameter optimization of the component model can be automatically, efficiently and accurately realized.
According to an exemplary embodiment of the present invention, the measured data and the simulation data each include time and a characteristic parameter corresponding to the time. The characteristic parameter is a voltage value and/or a current value. Taking parameter optimization of an insulated gate bipolar transistor (Insulated Gate Bipolar Transistor, IGBT) model as an example, the measured data may include: time d, voltage amplitude v (d), voltage slope
According to one embodiment of the invention, the fitness of a single individual is based on fitness at a plurality of times, the fitness at a single time satisfying: q (d) =k1·a1+k2·a2.
Wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
For example, the number of the cells to be processed,k1>10k2. Where K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d. A (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d. That is, the fitness calculation in this embodiment may include three types of fittable ways: only voltage, only current, voltage and current together. Examples of IGBTs listed herein are examples of voltages only.
Taking an IGBT model as an example,n represents the nth individual. Where d represents time, K (d) represents the measured voltage slope of time d, K (dN) represents the simulated voltage slope of time d, v (d) represents the measured voltage amplitude of time d, v (dN) represents the simulated voltage amplitude of time d, and K1 and K2 are weight coefficients.
Wherein the fitness of a single individual may be the sum of fitness at multiple times.
According to one embodiment of the invention, process 104 may be implemented by performing the following processes in a loop: a. performing simulation test on individuals in the initial population under the simulation test environment to obtain simulation data of each individual; b. determining the fitness of the individual based on the measured data and the simulation data of the individual; c. selecting parents from the initial population according to the fitness of individuals; d. new individuals are generated based on the parents and genetic operators to update the initial population. In the above cycle, if the end condition is satisfied, the individual with the highest fitness is selected from the initial population as the target individual. For example, after step b, if the iteration number of the initial population reaches the set value, the individual with the highest fitness is selected from the latest initial population as the target individual. Or after the step c, if the iteration number of the initial population reaches a set value, selecting the individual with the highest fitness from the parents as a target individual.
Fig. 2 is a flow chart of a method for optimizing parameters of a component model according to an exemplary embodiment of the present invention.
200: and establishing an EMC simulation device model. For main components in the EMC experimental object, a mathematical formula model is taken as a main component and SPICE language is taken as an auxiliary component, and an EMC simulation device model (namely a component model to be optimized) is constructed according to an official document.
Taking an IGBT model as an example, static mathematical modeling of the IGBT uses a Schichman-Hodges model modeling, and from the MOSFET, in order to calculate the amount of current flowing through the static MOSFET, it is necessary to distinguish between the linear amplification region and the saturation region of the MOSFET. The saturation voltage is defined as (the modeling process is the prior art, the formula symbol is understood by conventional knowledge, and the description is omitted here
When the linear region is transited to the saturation region, the drain current is
In the linear region, the drain current satisfies:
when in the saturation region, satisfies:
I D ＝I sat *(1+KLM*V DS )
the current IC flowing through the IGBT directly satisfies:
Ic＝BN*IB
IB satisfies the calculation formula:
the above unknown parameters are combined with the manufacturer-supplied data manual to directly obtain an initial model which is not optimized. The Spice language can assist in importing mathematical formulas into simulation software for circuit calculation.
202: actual test results in measured data.
Taking an IGBT model as an example, a test circuit is built in practice to test the voltage characteristics of the two ends of the collector and the emitter of the IGBT, and the measured data are listed as a data table. The table contains information about the time d, the voltage amplitude v (d), the voltage slopeThe actual measurement data plays a role in guiding the optimization of the device, and the effect is realized by comparing the data obtained by simulation with the actual measurement data.
204: a test circuit is built in a simulator and a simulation device model is put in the test circuit. For example, an IGBT model that has been designed using the spice language assistance is extracted and put into an analog test circuit that coincides with an actual circuit.
206: and optimizing and fitting the simulation device model by using a genetic algorithm. For a detailed description of this process, please refer to the embodiment shown in fig. 3.
208: and obtaining optimal model parameters.
210: outputting the optimized simulation device model. And updating the optimized simulation device model according to the model parameters output by 208.
Alternatively, in one embodiment of the invention, because 206 would result in the target individual, 208 and 210 may be replaced by: and outputting the target individual as an optimized simulation device model.
Fig. 3 is a flow chart of optimizing parameters of a component model using a genetic algorithm according to an exemplary embodiment of the present invention. As shown in fig. 3, the method includes:
300: and extracting key parameters of the simulation device model. For example, key parameters are extracted according to a build formula. As built in FIG. 2, IGBT model, V sat 、I sat 、M FE T、A FET Etc. can be extracted as key parameters. More specifically, taking an IGBT model designed under the simmerer module of Ansys corporation as an example, its main parameters include tens of key parameters such as GE-side capacitance coefficient, CE-side capacitance coefficient, FET transfer constant, FET pinch-off voltage, and the like. Assuming that c (for example, 4 to 10, or several tens of, depending on the actually constructed formula) key parameters are extracted therefrom as optimization targets, each key parameter is recorded as GA as one gene of the model, so that one IGBT model has c GA, which are sequentially recorded as GA (c).
301: the measured slope k (d), time d, and voltage magnitude v (d) are determined based on the measured data. As an evaluation factor of the fitness formula.
302: m individuals are created as an initial population, t=0 is set, and the maximum T of T is set. Where T represents the current algebra and T represents the maximum algebra.
For example, M IGBT models are created based on the initial IGBT model expanding in a random manner. The value of GA (c) inside each model is completely random.
303: and M individuals synchronously perform simulation operation, and the fitness evaluation of each individual is obtained according to a fitness formula.
Specifically, the M IGBT models are simulated in parallel in simulation software, data obtained by the Nth IGBT model in the simulation software are set as a table, the table contains information including time d (N), voltage amplitude v (dN) and voltage slope k (dN), the time d (N) corresponds to the actual measurement time d, and then fitness calculation is carried out:
the K1 and K2 coefficients are weight coefficients, and generally K1 is more than 10K2.
Extending the fitness calculation to the whole time period is to add all fitness, i.e.:
Q(N)＝Q(0,N)+Q(1,N)+Q(2,N)+……Q(d,N)
after the current generation is calculated, M fitness degrees Q (N) exist. Then the winner and winner elimination operation in the genetic algorithm can be performed.
304: and (5) evaluating and processing the superior and inferior jigs.
Specifically, the M models may be arranged from small to large according to the Q (N) values, and only the model with the Q (N) value in the first a% of the M models is taken as the parent, and the other models are deleted. In this case, a may be 10 to 40, or any other value. The larger the value of a, the faster the algorithm convergence rate and the higher the algorithm efficiency, but the result local optimal solution and the lack of mutation amount may be trapped. The selection of specific numerical values can be determined by self design according to the needs.
305: new individuals are generated based on the parents and genetic operators. Wherein genetic operators include selection and crossover.
Specifically, under the premise that parent individuals cannot repeatedly select, pairing every two pairs for crossover operation. For example, 2 parents are randomly selected. The GA inside 2 parents is crossed, and the crossing position can be any position in the range of [1, c-1 ]. The GA in this section after crossing will be transformed into the GA of another parent. Until the sum of the numbers of individuals remaining from the new and old populations returns to M.
306: individual mutations. Mutation is also one of the genetic operators.
Each GA of the new individuals will be mutated according to the probability of mutation P. GA mutations will mutate to random numbers.
In other embodiments of the present invention, steps 306 and 305 may be combined into one step, or step 306 may be omitted. In addition, individual mutations may be performed on all of the M models obtained in step 305.
The first iteration has ended, the evolution of the value t=t+1, the flow returns to step 303 and the execution is repeated.
In the above repeated execution, after step 304, it is determined whether T exceeds T. If so, then execution 307: and outputting the parameter value of the model with the highest fitness. For example, the values of the key parameters (the aforementioned c key parameters) of the model with the highest fitness are output. At this time, the simulation device with the highest fitness is used as an optimal individual to output key coefficients, and then the component model can be built again by using the key coefficients to obtain an optimized component model. Of course, the optimal individual may be directly output as the optimized component model. The optimized component model is placed in EMC simulation engineering, can serve as an interference source to reflect the magnitude of external electromagnetic interference (EMI) noise, and has practical guiding significance for the construction of EMC simulation projects.
Fig. 4 is a schematic view of a parameter optimization apparatus of a component model according to an exemplary embodiment of the present invention. As shown in fig. 4, the apparatus includes:
the actual measurement data acquisition module: the method is used for acquiring the actual measurement data of the component in the actual test environment. An initialization module: and the method is used for extracting parameters from the component model as genes and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values. And an optimization module: and the genetic algorithm is used for determining target individuals based on the initial population, and the genetic value of the target individuals is the optimized parameter of the component model. The fitness of the individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment.
According to an embodiment of the present invention, the measured data and the simulation data each include time and a component characteristic parameter value corresponding to the time. The fitness of an individual is based on fitness at a plurality of times, the fitness at a single time satisfying:
Q(d)＝K1·A1+K2·A2；
wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
For example, the number of the cells to be processed,k1＞10k2；
where K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d. A (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d.
According to one embodiment of the invention, the optimization module may comprise: the simulation test sub-module is used for performing simulation test on the individuals in the initial population under the simulation test environment to obtain simulation data of the individuals; the fitness determination submodule is used for determining fitness of an individual based on the actual measurement data and the simulation data of the individual; the parent selection submodule is used for selecting parents from the initial population according to the fitness of individuals; a population updating sub-module for generating a new population based on the parents and genetic operators to update the initial population; and the judging sub-module is used for selecting the individual with the highest fitness from the initial population as the target individual when the ending condition is met.
Fig. 5 is a schematic diagram of an electronic device according to an exemplary embodiment of the invention. As shown in fig. 5, the electronic device at least includes a processor and a memory, and may further include a communication component, a sensor component, a power supply component, a multimedia component, and an input/output interface according to actual needs. The memory, the communication component, the sensor component, the power component, the multimedia component and the input/output interface are all connected with the processor. The memory may be a Static Random Access Memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, etc., and the processor may be a Central Processing Unit (CPU), a Graphics Processor (GPU), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processing (DSP) chip, etc. Other communication components, sensor components, power components, multimedia components, etc. may be implemented using common components and are not specifically described herein.
In this embodiment, the electronic device includes: a memory for storing computer instructions; and the processor is used for reading and executing the computer instructions from the memory so as to realize the optimization of the parameters of the component model. For specific optimization logic, please refer to the foregoing, and details are not described herein.
Fig. 6 is a schematic diagram of a component simulation apparatus according to an exemplary embodiment of the present invention. Referring to fig. 6, the simulation apparatus includes: the device comprises a component model/modeling module, a parameter optimizing device, an updating module and a simulation module.
In this embodiment, the component model may be pre-built or imported, and the modeling module may support a technician to build the component model. In this embodiment, both the component model and the modeling module are available or available according to the prior art, and are not limited herein.
The parameter optimizing device may be a parameter optimizing device provided in the embodiment shown in fig. 4, or may be a device for implementing parameter optimization based on the embodiments shown in fig. 1 to 3. And the updating module is used for optimizing the component model according to the parameters determined by the parameter optimizing device to obtain an optimized component model. And the simulation module performs simulation processing based on the optimized component model. In one embodiment of the present invention, the updating module may be omitted, and at this time, the simulation module directly performs the simulation processing with the target individual obtained by the parameter optimization device as the optimized component model.
The figures and detailed description of the invention referred to above as examples of the invention are intended to illustrate the invention, but not to limit the meaning or scope of the invention described in the claims. Accordingly, modifications may be readily made by one skilled in the art from the foregoing description. In addition, one skilled in the art may delete some of the constituent elements described herein without deteriorating the performance, or may add other constituent elements to improve the performance. Furthermore, one skilled in the art may vary the order of the steps of the methods described herein depending on the environment of the process or equipment. Thus, the scope of the invention should be determined not by the embodiments described above, but by the claims and their equivalents.
While the invention has been described in connection with what is presently considered to be practical, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method for optimizing parameters of a component model, the method comprising:
obtaining actual measurement data of the component in an actual test environment;
extracting parameters from the component model as genes, and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values;
determining a target individual based on the initial population by utilizing a genetic algorithm, wherein the gene value of the target individual is the optimized parameter of the component model;
the fitness of individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment;
the fitness of a single individual is based on fitness at a plurality of times, the fitness at a single time satisfying:
Q(d)＝K1·A1+K2·A2；
wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the measured data and the simulation data comprise time and characteristic parameters corresponding to the time;
the characteristic parameter comprises a voltage value and/or a current value.
3. A method according to claim 1 or 2, characterized in that,
wherein K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d;
where a (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d.
4. The method of claim 1, wherein said determining a target individual based on said initial population and using a genetic algorithm comprises:
the following steps are circularly executed:
performing simulation test on individuals in the initial population under the simulation test environment to obtain simulation data of each individual,
determining the fitness of the individual based on the measured data and the simulation data of the individual,
selecting parents from the initial population according to the fitness of individuals,
generating new individuals based on the parents and genetic operators to update the initial population;
in the cycle, if an end condition is satisfied, selecting the individual with the highest fitness from the initial population as the target individual.
5. A device for optimizing parameters of a component model, the device comprising:
the actual measurement data acquisition module: the method comprises the steps of obtaining actual measurement data of the component in an actual test environment;
an initialization module: the method comprises the steps of extracting parameters from the component model as genes, and establishing an initial population, wherein individuals in the initial population have randomly allocated gene values;
and an optimization module: the genetic algorithm is used for determining a target individual based on the initial population, and the gene value of the target individual is the optimized parameter of the component model;
the fitness of individuals in the initial population is determined based on the actual measurement data and simulation data obtained under a simulation test environment, and the simulation test environment is consistent with the actual test environment;
the fitness of a single individual is based on fitness at a plurality of times, the fitness at a single time satisfying:
Q(d)＝K1·A1+K2·A2；
wherein d represents time, A1 is determined according to the ratio of the slope of the simulation characteristic parameter to the slope of the measured characteristic parameter at time d, A2 is determined according to the ratio of the simulation characteristic parameter to the measured characteristic parameter at time d, and K1 and K2 are weight coefficients.
6. The apparatus of claim 5, wherein the device comprises a plurality of sensors,
the measured data and the simulation data comprise time and characteristic parameters corresponding to the time;
the characteristic parameter comprises a voltage value and/or a current value.
7. The apparatus of claim 5 or 6, wherein the device comprises a plurality of sensors,
wherein K (d) represents the measured voltage slope of time d and K (dN) represents the simulated voltage slope of time d, or K (d) represents the measured current slope of time d and K (dN) represents the simulated current slope of time d;
where a (d) represents the measured voltage amplitude of time d and a (dN) represents the simulated voltage amplitude of time d, or a (d) represents the measured current amplitude of time d and a (dN) represents the simulated current amplitude of time d.
8. The apparatus of claim 5, wherein the optimization module comprises:
the simulation test sub-module is used for performing simulation test on the individuals in the initial population under the simulation test environment to obtain simulation data of the individuals;
the fitness determination submodule is used for determining fitness of an individual based on the actual measurement data and the simulation data of the individual;
the parent selection submodule is used for selecting parents from the initial population according to the fitness of individuals;
a population updating sub-module for generating a new population based on the parents and genetic operators to update the initial population;
and the judging sub-module is used for selecting the individual with the highest fitness from the initial population as the target individual when the ending condition is met.
9. An electronic device for optimizing parameters of a component model, the electronic device comprising:
a memory for storing computer instructions;
a processor for reading and executing computer instructions from the memory to implement the method of optimizing parameters of a component model as claimed in any one of claims 1 to 4.
10. A component simulation apparatus, comprising:
the modeling module is used for generating a component model and/or supporting the component model;
a parameter optimization apparatus of a component model according to any one of claims 5 to 8;
and the simulation module is used for simulating based on the optimized component model.
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