CN117408206B - Electroacoustic transducer broadband impedance matching design method based on pareto optimization - Google Patents
Electroacoustic transducer broadband impedance matching design method based on pareto optimization Download PDFInfo
- Publication number
- CN117408206B CN117408206B CN202311716904.XA CN202311716904A CN117408206B CN 117408206 B CN117408206 B CN 117408206B CN 202311716904 A CN202311716904 A CN 202311716904A CN 117408206 B CN117408206 B CN 117408206B
- Authority
- CN
- China
- Prior art keywords
- impedance matching
- pareto
- matching network
- value
- power amplifier
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005457 optimization Methods 0.000 title claims abstract description 28
- 230000035772 mutation Effects 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 5
- 239000003990 capacitor Substances 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims 1
- 238000011156 evaluation Methods 0.000 abstract description 4
- 238000013178 mathematical model Methods 0.000 abstract description 4
- 230000004044 response Effects 0.000 description 8
- 230000006872 improvement Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000013585 weight reducing agent Substances 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
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
-
- 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
- G06F30/373—Design optimisation
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H7/00—Multiple-port networks comprising only passive electrical elements as network components
- H03H7/38—Impedance-matching networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Amplifiers (AREA)
Abstract
The invention discloses an electroacoustic transducer broadband impedance matching design method based on pareto optimization, which comprises the following steps: parameters such as impedance in an operating frequency band of an input transducer are established, a comprehensive evaluation mathematical model related to load power factors, power density and the like is taken as an optimization target, a constrained multi-target differential evolution algorithm is adopted, parameters of a matching network element are taken as decision variables, upper limits of voltage and current of each branch of a power amplifier output and a matching network are taken as constraint conditions, matching networks of different orders and types are respectively solved to obtain pareto fronts, individuals contained in all the pareto fronts are subjected to non-dominant sorting according to three optimization targets, and the final pareto fronts are output to be selected by a user as an alternative scheme. The invention has the advantages of multiple traversal topologies, high convergence speed, difficult sinking into local optimum and optimal pareto of the obtained matching network scheme, and has important significance for the optimization design of the order, topology and parameters of the impedance matching network of the electroacoustic transducer.
Description
Technical Field
The invention relates to a broadband impedance matching design method of a high-power electroacoustic transducer based on pareto optimization.
Background
Because the transducer itself has a low load power factor and the impedance parameter and electroacoustic conversion efficiency within the operating frequency band vary non-linearly with frequency, a cascaded impedance matching network is required between the power amplifier and the electroacoustic transducer. The basic requirement of the broadband impedance matching network is to ensure higher load power factor in the working frequency band of the transducer, improve the working bandwidth of the electroacoustic transducer and the electric energy utilization rate of the power amplifier, and facilitate the miniaturization and the weight reduction of the electroacoustic transducer transmitting system. In addition, the good impedance matching network is also suitable for practical requirements, such as reducing the power density of the impedance matching network, taking the internal power supply condition of the ship into consideration to play a role in regulating voltage, making up the gap of electroacoustic conversion efficiency of the transducer at different frequency points, and enabling the sound source level under the conditions of transmitting voltage response and constant output voltage of the power amplifier to be as flat as possible.
The invention establishes a comprehensive evaluation mathematical model about the output power factor, the response fluctuation of the transmitting voltage and the power density of the impedance matching network of a power amplifier aiming at the broadband impedance matching network design applied to a high-power electroacoustic transducer, adopts a multi-target differential evolution algorithm considering constraint, takes the matching network parameter processed by per unit as a decision variable, takes the output power factor of the power amplifier, reduces the response fluctuation of the transmitting voltage in the working frequency band of the transducer and reduces the volume of the impedance matching network as an optimization target, and takes the upper limit of the voltage and the current of each branch of the power amplifier output and the impedance matching network as a constraint condition. And respectively optimizing T-type and n-type impedance matching networks with different orders to obtain pareto fronts, and then performing non-dominant sorting on individuals contained in all the pareto fronts according to three objective functions to output a final pareto front as an alternative impedance matching network topology and parameter scheme for user selection. The method has important significance for optimizing the design of the order, topology and parameters of the impedance matching network of the electroacoustic transducer.
Disclosure of Invention
In order to solve the technical problems, the invention provides a broadband impedance matching design method of a high-power electroacoustic transducer based on pareto optimization, which derives a comprehensive evaluation mathematical model about the output power factor of a power amplifier, the response fluctuation of a transmitting voltage and the power density of an impedance matching network, adopts a multi-objective differential evolution algorithm considering constraint, searches for the pareto optimal solution of the order, topology and element parameters of the impedance matching network for selection by a user, effectively realizes the broadband impedance matching of the transducer, simultaneously enables the response of the transmitting voltage to be flatter, and reduces the volume of the impedance matching network.
The technical scheme for solving the technical problems comprises the following steps:
a design method for broadband impedance matching of an electroacoustic transducer based on pareto optimization comprises the following steps:
step one: setting an optimization target: maximizing the minimum power factor in the operating frequency band, minimizing the sound source level fluctuation under the constant voltage output condition of the power amplifier, and minimizing the volume of the impedance matching network;
step two: taking the per unit value of the topology parameter as a decision variable, and adopting multi-objective real number coding: in order to find the optimal impedance matching network topology and element parameters of the specified order and type in the solution space feasible domain, each decision variable needs to correspond to one element in the matching network, the positive and negative of the decision variable correspond to the inductance and the capacitance respectively, and the absolute value of the decision variable corresponds to the element parameter value after per unit, namely the inductance value or the capacitance value after per unit;
step three: assigning an impedance matching network orderPerforming multi-objective optimization on the per-unit impedance matching network element parameters by adopting a differential evolution algorithm, and operating the differential evolution algorithm to obtain ∈10 by taking the voltage and current upper limit of each branch in the power amplifier output end and the impedance matching network as constraint conditions>Pareto front edge of the T-shaped and pi-shaped impedance matching network;
step four: designating different impedance matching network orders, repeatedly executing a differential evolution algorithm to obtain pareto fronts of different orders and types, performing non-dominant sorting on individuals contained in all the pareto fronts, and outputting a final pareto front for a user to select.
Further improvement, the first step comprises the following steps:
is provided withThe step impedance matching network is composed of->Each branch is formed by an inductor or a capacitor; the minimum power factor of the output end of the power amplifier is +.>The effective value range of the power amplifier output voltage under the condition that the output sound source level of the transducer is constant is +.>The impedance matching network is +.>The method comprises the steps of carrying out a first treatment on the surface of the When solving the objective function of the individual, the transducer sound source level is set up first>In the operating frequency bandInner->Average sound source level working at rated voltage at each frequency point, and back-pushing effective value +.>The method comprises the steps of carrying out a first treatment on the surface of the Calculate->Average value at all frequency points +.>Taking the actual output voltage of the power amplifierCalculating actual sound source level fluctuation and actual maximum voltage and current of each branch; setting an optimization target as follows:
wherein,is the +.>Frequency point(s)>Output voltage effective value of power amplifier under constant condition of output sound source level of transducer>Is the power factor of the output end of the power amplifier, +.>And->The output voltage of the power amplifier is respectively equal to +.>Under the condition->Maximum voltage and maximum current effective values of the branches at all frequency points,the inductance and capacitance are selected based on the imported inductance-capacitance database with the element parameter value, maximum voltage, maximum current as inputs, and a function of the element volume is output.
Further improvement, the method for obtaining the per unit value of the topology parameter in the second step is as follows:
specifying the maximum inductance value allowed by the impedance matching networkAnd maximum capacitance +.>Respectively used as reference values of an inductance value and a capacitance value; element parameters and decision variables->The relationship between them is expressed as:
wherein,indicate->Decision variables->And->Respectively represent +.>The inductance or capacitance of the branch circuit has a known value to obtain。
Further improvement, the third step comprises the following steps:
setting constraint conditions:
，
wherein,maximum effective value of allowable output voltage for power amplifier, < >>The maximum effective values allowed by the output current of the power amplifier and the current of each branch are respectively obtained; />Respectively +.>The branch is at%>Voltage and current effective values at the frequency points; the differential evolution algorithm comprises the following steps: let the current evolution algebra be->Every generation of population->The number of individuals is->Setting the maximum evolutionary algebra as +.>The method comprises the steps of carrying out a first treatment on the surface of the Carrying out mutation, intersection and selection on each generation of population in sequence; at each variationIn the crossing process, a mutation crossing operator randomly selects between DE/rand-to-best/1/bin and DE/current-to-rand/1 to obtain a mutation containing +.>Test vector of individual>The method comprises the steps of carrying out a first treatment on the surface of the During each selection, the +.>Population of generationsAnd test vector->Two orders were made for all individuals in (a): first time according to the pareto dominance relation>Ranking individual individuals, within each ranking, in descending order according to crowding distance; second time according to constraint regulation principleRanking individual individuals, within each ranking, in descending order according to crowding distance; weighting and summing the ranking values of the two orders to obtain the final ranking, taking the former +.>The first population is used as a new generation population, the weighting value changes along with the evolution algebra, the pareto ordering in the initial stage is dominant, and the constraint dominant ordering proportion is larger and larger along with the increase of the evolution algebra; differential evolution algorithm until the algebra reaches the maximum algebra +.>Stopping operation and outputting the pareto front of the last generation population.
Further improvement, the fourth step comprises the following steps:
and according to the three objective function values of the optimization target, non-dominant sorting is carried out on the pareto fronts obtained by optimizing T-type and n-type impedance matching networks with different orders, and the final pareto fronts are output for a user to select.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a broadband impedance matching design method of a high-power electroacoustic transducer based on pareto optimization, which is characterized in that a comprehensive evaluation mathematical model about the output power factor of a power amplifier, the response fluctuation of transmitting voltage and the power density of an impedance matching network is firstly established, a multi-objective differential evolution algorithm considering constraint is adopted to find the pareto optimal solution of the order, topology and element parameters of the impedance matching network for selection by a user, so that the broadband impedance matching of the transducer is effectively realized, the response of the transmitting voltage is flatter, and the volume of the impedance matching network is reduced.
The invention is further described below with reference to the accompanying drawings.
Drawings
FIG. 1a is a schematic diagram of a method according to the inventionnThe cascaded structure diagram of the order T-shaped and impedance matching network, the power amplifier and the electroacoustic transducer.
FIG. 1b is a schematic illustration of the method of the present inventionnAn pi-type and impedance matching network, a power amplifier and an electroacoustic transducer cascading structure diagram.
Fig. 2 is a flow chart of a method for designing an impedance matching network according to an embodiment of the invention.
FIG. 3 is a flow chart of a multi-objective differential evolution algorithm for element order, topology and parameter optimization in accordance with one embodiment of the present invention.
FIG. 4 is a flowchart of an algorithm for calculating a minimum inductance volume according to inductance and current demand according to an embodiment of the invention.
Detailed Description
FIGS. 1a and 1b are schematic illustrations of the process of the present inventionThe cascade structure of the T-shaped and pi-shaped impedance matching network, the power amplifier and the electroacoustic transducer. Wherein (1)>The transducers are at->Equivalent resistance and equivalent capacitance at the frequency points, +.>The output voltage and the current effective value of the power amplifier and the voltage and the current effective value of the two ends of the transducer are respectively. Let->The step impedance matching network is composed of->Each branch is an inductor or a capacitor, and all branches in the impedance matching network are numbered according to the sequence from near to far with the transducer; the branch closest to the transducer in the T-shaped matching network is in series connection with the transducer, and the branch closest to the transducer in the pi-shaped matching network is in parallel connection with the transducer.
Fig. 2 is a flowchart of a method for designing an impedance matching network according to an embodiment of the present invention, which includes the following implementation steps:
step one: input transducer parameters including impedance within the operating frequency band, transmit voltage response parameters, etc.;
step two: setting an optimization problem according to the impedance matching requirement of actual engineering: the method comprises the steps of taking the maximum minimum power factor in an operation frequency band, the minimum sound source level fluctuation under the constant voltage output condition of a power amplifier and the minimum impedance matching network volume as optimization targets and taking the upper limit of the voltage and the current of each branch as constraint conditions;
step three: specifying impedance matching network type and orderPerforming multi-objective optimization on the per-unit impedance matching network element parameters by adopting a differential evolution algorithm to obtain +.>Pareto front edge of the T-shaped and pi-shaped impedance matching network;
step four: specifying different impedance matching network ordersAnd repeatedly executing a differential evolution algorithm to obtain pareto fronts of different orders and types, performing non-dominant ordering on individuals contained in all the pareto fronts, and outputting the final pareto fronts for a user to select.
FIG. 3 is a flow chart of a multi-objective differential evolution algorithm with consideration constraints for element order, topology and parameter optimization in accordance with one embodiment of the present invention. The specific implementation steps are as follows:
step one: initializing constants including population sizeNumber of decision variables->Equal to the order of the impedance matching network->Number of objective functions->Maximum value of inductance and capacitance allowed in impedance matching network +.>And->Maximum algebra of evolutionThe method comprises the steps of carrying out a first treatment on the surface of the Initializing variables including current algebra ++>Randomly generating an initial population;
step two: calculating target functions and constraint violation degrees of all individuals in the population;
step three: judging whether the termination condition is satisfied, i.e. the current evolution algebraWhether or not to be greater than or equal to the maximum evolution algebraIf yes, executing the fourth step, and if not, executing the ninth step;
step four: randomly selecting a scaling factor in the set 0.6,0.8,1.0The crossover probability +.is randomly chosen among the set {0.1,0.2,1.0}>；
Step five: performing mutation crossover to obtain offspring populationOffspring population->The%>Personal decision vector->The random is generated by adopting the following two cross selection operators:
(1) DE/rand-to-best/1/bin
，
(2) DE/current-to-rand/1
，
in the method, in the process of the invention,representing the current evolutionary algebra @, ->Mutation vector generated for mutation operation, +.>Indicate->The code number is->Is a parent decision vector of (1), subscript +.>For the collection->Random integer of>Representing test vectors generated through the crossover operation.
Step six: taking outFor->Is->The individuals were ranked twice: first time according to the pareto dominance relation>Individuals are ranked, and within each ranking, individuals are sorted in descending order according to crowding distance, < +.>The ranking value of the individual is marked +.>The method comprises the steps of carrying out a first treatment on the surface of the Second time according to constraint rulesFor->Individuals are ranked, and within each ranking, individuals are sorted in descending order according to crowding distance, < +.>The ranking value of the individual is marked +.>；
Step seven: calculating weighting coefficients
In the method, in the process of the invention,representing the current evolutionary algebra @, ->Is the maximum algebra of evolution.
Weighting and summing the twice-sequenced ranking values to obtain the comprehensive ranking
In the method, in the process of the invention,respectively +.>Individual ranking values and composite ranking values based on crowding distance, constraint governance principle.
Before taking the comprehensive ranking valuePerson as->Parent population, i.e.)>The weighting value changes along with the evolution algebra, the pareto ordering at the initial stage is dominant, the constraint dominant ordering proportion is larger and larger along with the increase of the evolution algebra;
step eight:jumping to the third step;
step nine: if the termination condition is satisfied, the pareto front is output。
FIG. 4 is a flowchart of an algorithm for calculating a minimum inductance volume according to inductance and current demand according to an embodiment of the invention. The specific implementation steps are as follows:
step one: inputting magnetic core parameters in a magnetic core manual, including magnetic core size, inductance coefficient and magnetic permeability reduction coefficient, wherein the magnetic core parameters comprise inductance and working frequency of the magnetic core;
step two: calculating the wire diameter and the required stock number according to the working frequency and the through-flow requirement;
step three: calculating upper limit of winding turns according to magnetic core sizeAnd lower limit->Calculating the maximum number of turns allowed by each magnetic core saturation degree according to the magnetic core permeability reduction requirement>；
Step four: the number of the magnetic cores is calculated from small to large to reach the number of turns required by the design inductance until at least one scheme simultaneously meets the upper limit and the lower limit;
step five: and outputting the inductance volume.
Claims (4)
1. The design method for broadband impedance matching of the electroacoustic transducer based on pareto optimization is characterized by comprising the following steps:
step one: setting an optimization target: maximizing the minimum power factor in the operating frequency band, minimizing the sound source level fluctuation under the constant voltage output condition of the power amplifier, and minimizing the volume of the impedance matching network;
step two: taking the per unit value of the topology parameter as a decision variable, and adopting multi-objective real number coding: in order to find the optimal impedance matching network topology and element parameters of the specified order and type in the solution space feasible domain, each decision variable needs to correspond to one element in the matching network, the positive and negative of the decision variable correspond to the inductance and the capacitance respectively, and the absolute value of the decision variable corresponds to the element parameter value after per unit, namely the inductance value or the capacitance value after per unit;
step three: assigning an impedance matching network orderPerforming multi-objective optimization on the per-unit impedance matching network element parameters by adopting a differential evolution algorithm, and operating the differential evolution algorithm to obtain ∈10 by taking the voltage and current upper limit of each branch in the power amplifier output end and the impedance matching network as constraint conditions>The pareto front edge of the T-shaped and pi-shaped impedance matching network comprises the following specific steps:
setting constraint conditions:
；
wherein,maximum effective value of allowable output voltage for power amplifier, < >>The maximum effective values allowed by the output current of the power amplifier and the current of each branch are respectively obtained; />Respectively +.>The branch is at%>Voltage and current effective values at the frequency points; the differential evolution algorithm comprises the following steps: let the current evolution algebra be->Every generation of population->The number of individuals is->Setting the maximum evolutionary algebra as +.>The method comprises the steps of carrying out a first treatment on the surface of the Carrying out mutation, intersection and selection on each generation of population in sequence; in each mutation crossover process, mutation crossover operator randomly selects between DE/rand-to-best/1/bin and DE/current-to-rand/1 to obtain a mutation crossover operator containing->Test vector of individual>The method comprises the steps of carrying out a first treatment on the surface of the During each selection, the +.>Substitution population->And test vector->Two orders were made for all individuals in (a): first time according to the pareto dominance relation>Ranking individual individuals, within each ranking, in descending order according to crowding distance; second time according to constraint rules>Ranking individual individuals, within each ranking, in descending order according to crowding distance; weighting and summing the ranking values of the two orders to obtain the final ranking, taking the former +.>The first population is used as a new generation population, the weighting value changes along with the evolution algebra, the pareto ordering in the initial stage is dominant, and the constraint dominant ordering proportion is larger and larger along with the increase of the evolution algebra; differential evolution algorithm until the algebra reaches the maximum algebra +.>Stopping operation and outputting the pareto front edge of the last generation population;
step four: designating different impedance matching network orders, repeatedly executing a differential evolution algorithm to obtain pareto fronts of different orders and types, performing non-dominant sorting on individuals contained in all the pareto fronts, and outputting a final pareto front for a user to select.
2. The method for designing broadband impedance matching of an electroacoustic transducer based on pareto optimization according to claim 1, wherein the first step comprises the steps of:
is provided withThe step impedance matching network is composed of->Each branch is formed by an inductor or a capacitor; the minimum power factor of the output end of the power amplifier is +.>The effective value range of the power amplifier output voltage under the condition that the output sound source level of the transducer is constant is +.>The impedance matching network is +.>The method comprises the steps of carrying out a first treatment on the surface of the When solving the objective function of the individual, the transducer sound source level is set up first>In the operating band->Average sound source level working at rated voltage at each frequency point, and back-pushing effective value +.>The method comprises the steps of carrying out a first treatment on the surface of the Calculate->Average value at all frequency points +.>Taking the actual output voltage of the power amplifierCalculating actual sound source level fluctuation and actual maximum voltage and current of each branch; setting an optimization target as follows:
；
wherein,is the +.>Frequency point(s)>Output voltage effective value of power amplifier under constant condition of output sound source level of transducer>Is the power factor of the output end of the power amplifier, +.>And->Respectively, the output voltages of the power amplifier are constantUnder the condition->Maximum voltage and maximum current effective values of the branches at all frequency points,the inductance and capacitance are selected based on the imported inductance-capacitance database with the element parameter value, maximum voltage, maximum current as inputs, and a function of the element volume is output.
3. The method for designing broadband impedance matching of an electroacoustic transducer based on pareto optimization according to claim 1, wherein the method for obtaining the per unit value of the topological parameter in the step two is as follows:
specifying the maximum inductance value allowed by the impedance matching networkAnd maximum capacitance +.>Respectively used as reference values of an inductance value and a capacitance value; element parameters and decision variables->The relationship between them is expressed as:
；
wherein,indicate->Decision variables->And->Respectively represent +.>The inductance or capacitance of the branch circuit has a known value to obtain。
4. The method for designing broadband impedance matching of an electroacoustic transducer based on pareto optimization according to claim 1, wherein the fourth step comprises the following steps:
and according to the three objective function values of the optimization target, non-dominant sorting is carried out on the pareto fronts obtained by optimizing T-type and n-type impedance matching networks with different orders, and the final pareto fronts are output for a user to select.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716904.XA CN117408206B (en) | 2023-12-14 | 2023-12-14 | Electroacoustic transducer broadband impedance matching design method based on pareto optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716904.XA CN117408206B (en) | 2023-12-14 | 2023-12-14 | Electroacoustic transducer broadband impedance matching design method based on pareto optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117408206A CN117408206A (en) | 2024-01-16 |
| CN117408206B true CN117408206B (en) | 2024-04-16 |
Family
ID=89492917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311716904.XA Active CN117408206B (en) | 2023-12-14 | 2023-12-14 | Electroacoustic transducer broadband impedance matching design method based on pareto optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117408206B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975812A (en) * | 2017-12-27 | 2019-07-05 | 中国科学院声学研究所 | A kind of sound source depth of explosive sound and the calculation method of sound source level |
| CN117034839A (en) * | 2023-08-21 | 2023-11-10 | 湖南大学 | Broadband impedance matching design method for high-power electroacoustic transducer constant-current driving system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6957200B2 (en) * | 2001-04-06 | 2005-10-18 | Honeywell International, Inc. | Genotic algorithm optimization method and network |
| US20140086013A1 (en) * | 2012-09-25 | 2014-03-27 | Jeong Min Lee | Method for an equivalent circuit parameter estimation of a transducer and a sonar system using thereof |
| US9103905B2 (en) * | 2012-12-12 | 2015-08-11 | Agency For Defense Development | Sonar system and impedance matching method thereof |
-
2023
- 2023-12-14 CN CN202311716904.XA patent/CN117408206B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975812A (en) * | 2017-12-27 | 2019-07-05 | 中国科学院声学研究所 | A kind of sound source depth of explosive sound and the calculation method of sound source level |
| CN117034839A (en) * | 2023-08-21 | 2023-11-10 | 湖南大学 | Broadband impedance matching design method for high-power electroacoustic transducer constant-current driving system |
Non-Patent Citations (3)
| Title |
|---|
| Multiobjective Design Optimization of Extremely Low-Frequency Power Amplifier Considering Accuracy, Volume, and Reliability;rong han;《IEEE》;20221031;全文 * |
| 多层螺旋线圈电磁超声换能器优化设计及其实验研究;唐琴;石文泽;卢超;陈巍巍;张金;陈尧;黄祺凯;程进杰;;中南大学学报(自然科学版);20200726(第07期);全文 * |
| 多目标模拟退火算法求解混装线平衡与排序;姜东;唐秋华;李梓响;吴玲;;机械设计与制造;20180908(第09期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117408206A (en) | 2024-01-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ibrahim et al. | EliteNSGA-III: An improved evolutionary many-objective optimization algorithm | |
| CN102722613B (en) | Method for optimizing electronic component parameters in antenna broadband matching network by adopting genetic-simulated annealing combination | |
| CN109038545B (en) | Power distribution network reconstruction method based on differential evolution invasive weed algorithm | |
| CN113036772A (en) | Power distribution network topology voltage adjusting method based on deep reinforcement learning | |
| Rahmat et al. | Differential evolution ant colony optimization (DEACO) technique in solving economic load dispatch problem | |
| Kumar et al. | Optimal distribution network reconfiguration to improve the system performances using PSO with multiple-objectives | |
| CN110021940B (en) | Capacitor optimal configuration method based on improved particle swarm optimization | |
| Zobaa | Mixed-integer distributed ant colony multi-objective optimization of single-tuned passive harmonic filter parameters | |
| Deshmukh et al. | Optimal placement and sizing of distributed generator in distribution system using artificial bee colony algorithm | |
| CN114036669B (en) | Optimal design method for low-speed magnetic gear of high-power wind electromagnetic gear box | |
| CN117408206B (en) | Electroacoustic transducer broadband impedance matching design method based on pareto optimization | |
| Ribeiro et al. | A Posteriori Strategy to Identify Robust Solutions in the Many-Objective Design Optimization of an Axial-Flux Permanent Magnet Synchronous Generator | |
| Kumar et al. | Optimisation of voltage and frequency regulation in an isolated wind-driven six-phase self-excited induction generator | |
| CN117291004A (en) | Transformer parameter optimization design method based on MSPBO algorithm | |
| CN115310353B (en) | Power transformer design method based on rapid multi-objective optimization | |
| CN108053250B (en) | Genetic algorithm-based transformer price optimization method and device | |
| Dongmei et al. | Reactive power optimization by genetic algorithm integrated with reduced gradient method | |
| CN113346501B (en) | Power distribution network voltage optimization method and system based on brainstorming algorithm | |
| CN112734590B (en) | Two-stage regional power grid reactive power optimization method based on interior point method and dynamic programming method of variable domain search | |
| Liu et al. | Optimization design of amorphous metal distribution transformer based on improved fast and elitist multi-objective genetic algorithm | |
| Mansour et al. | Dynamic economic load dispatch of thermal power system using genetic algorithm | |
| Le Roux et al. | Distribution network reconfiguration using genetic algorithm and load flow | |
| CN112564138A (en) | Three-phase unbalanced reactive power optimization method and system thereof | |
| Shuqin et al. | Power system reactive power optimization based on adaptive particle swarm optimization algorithm | |
| Li et al. | Optimal design method for LLCL filters based on NSGA-III |
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 |