JP2005109638A - Optimizing system for object with multilayer frequency selection board assembled therein and optimizing program therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a frequency selecting board having enhanced convergence at optimizing processing and having a easily manufactured shape. <P>SOLUTION: An optimizing system is provided with: an optimizing processing means 2 for carrying out optimizing processing of various parameters of an object in which multilayer frequency selection boards F1, F2 being design objects are assembled by means of a prescribed optimizing method such as a genetic algorithm or an annealing method; and a shape database 3 for storing data e<SB>n</SB>(n=1, 2, ...) of various shapes of components for configuring the frequency selection boards F1, F2. The optimizing processing means is provided with: a component shape data selection section 2a for selecting the component shape data e<SB>n</SB>from the shape database 3; and processing sections 2b to 2g for applying the optimizing processing to the frequency selection boards F1, F2 by using the selected component shape data e<SB>n</SB>as the parameters. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to optimization of an object such as a radome incorporating a multi-frequency selection board (FSS: Frequency Selective Surface), and in particular, a multi-layer frequency selection board assembly that can improve the optimization of the multi-layer frequency selection board. The present invention relates to an optimization system for an embedded object, an optimization method thereof, and an optimization program thereof.

  In recent years, for example, the necessity of “stealth technology” to prevent the existence of fighters and battleships from being confirmed by others in defense-related technologies has been screamed, and frequency selection plates are used as one of such stealth technologies. ing.

  Here, the frequency selection plate is a periodic structure film having a characteristic (frequency selectivity) that transmits or reflects only a radio wave having a specific frequency. This is one of the effective techniques for achieving stealth of an object such as an electromagnetic window for reducing (Radar Cross Section). The frequency selection plate is also used as a broadband electromagnetic wave absorber by combining with a specific dielectric or magnetic material.

  An object in which this kind of multilayer frequency selection plate is incorporated (hereinafter referred to as “multilayer frequency selection plate built-in object”) is related to a component such as a frequency selection plate in the design stage in order to obtain the desired characteristics. Parameters are optimized and their shapes are determined. There are various methods for optimizing the parameters, but in recent years, a genetic algorithm (GA) is often used (see, for example, Non-Patent Document 1).

  Here, the optimization method using a genetic algorithm as an optimization method can set an arbitrary unit element shape for the frequency selection plate of the same size, and the power transmittance, reflectance, transmission Alternatively, an optimum solution of parameters satisfying the fitness function defined from the frequency of the reflected radio wave, the incident angle of the radio wave, and the like is obtained, and the shape of the multilayer frequency selection plate built-in object is set. For example, taking the above wave absorber as an example, the optimal solution of parameters such as impedance and patch / slot shape in the frequency selective plate layer, and the material (complex ratio) in the dielectric and magnetic layers. The shape is set by obtaining an optimal solution of parameters such as dielectric constant, complex relative permeability, etc.) and its thickness.

"Application of a Microgenetic Algorithm (MGA) to the Design of Broad-Band Microwave Absorbers Using Multiple Frequency Selective Surface Screens Buried in Dielectrics", IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, (USA), March 2002, Vol. 50, no. 3, p. 284-296

Here, in the above-described optimization method, when the number of variables increases (for example, in the above-described genetic algorithm, the code length of a binary-coded parameter (the number of digits of the parameter when expressed in binary)) becomes long. In other words, the convergence deteriorates in inverse proportion to this. Therefore, in the conventional optimization method, for example, in the case of a multilayer frequency selection plate built-in object having L _FSS frequency selection plates (generally N = 16 to 64) composed of N × N cells. In order to achieve optimization, a code length of N ² × L _FSS bits is required at least in the frequency selection plate layer, and the convergence is greatly deteriorated due to the length of the code length. It was. For this reason, conventionally, the calculation processing time required for optimization has greatly increased.

  In addition, in the electromagnetic wave absorber exemplified as the multilayer frequency selection plate built-in object above, it is generally preferable to gradually change the impedance in order to achieve high performance. The patch / slot shape of each frequency selection plate may be simplified and the unit cell size may be changed. However, the conventional optimization method can set an arbitrary shape for each frequency selection plate having a constant unit cell size, and has the disadvantage that a frequency selection plate having a complicated shape is created in each layer. there were. For example, a complicated shape such as an enclave in which conductors (FSS elements made of metal wires or the like for constituting a frequency selection plate (FSS)) are randomly arranged may be created. It was not easy to manufacture.

  Therefore, the present invention improves the inconveniences of the conventional example, improves the convergence at the time of optimization, reduces the processing time, and obtains a frequency selection plate having a shape that is actually easy to manufacture. It is an object of the present invention to provide an optimization system for a multilayer frequency selective plate built-in object, an optimization method thereof, and an optimization program thereof.

  In order to achieve the above object, according to the first aspect of the present invention, optimization of an object incorporating a multilayer frequency selection plate to be designed is performed using a predetermined optimization method such as a genetic algorithm or an annealing method. In an optimization system for a multilayer frequency selection plate built-in object, optimization processing means for performing optimization processing of various parameters of the multilayer frequency selection plate built-in object by the optimization method, and elements of the frequency selection plate And a shape database in which data of various shapes are stored. The optimization processing means includes an element shape data selection unit that selects element shape data from the shape database, and a processing unit that performs optimization processing of the frequency selection plate using the selected element shape data as a parameter. Provided.

  According to a second aspect of the present invention, in the optimization system according to the first aspect, in order for the processing unit to perform the optimization process of the frequency selection plate, the unit cell size ratio of the frequency selection plate is also a parameter. It is said.

  Furthermore, in order to achieve the above object, the invention according to claim 3 uses a predetermined optimization method such as a genetic algorithm or an annealing method to optimize an object incorporating a multilayer frequency selection plate to be designed. In the method for optimizing an object incorporated in a multi-layer frequency selection plate, the element shape data is selected from a shape database in which data of various shapes of elements constituting the frequency selection plate is stored, and the selected element shape The frequency selection plate is optimized using data as a parameter.

  In the invention according to claim 4, in the optimization method according to claim 3, the unit cell size ratio of the frequency selection plate is also used as a parameter when the frequency selection plate is optimized.

  Furthermore, in order to achieve the above-mentioned object, the invention according to claim 5 uses an optimization method such as a genetic algorithm or an annealing method to optimize an object incorporating a multilayer frequency selection plate to be designed. In a program for optimizing a multilayer frequency selection plate built-in object having various instructions for causing the central processing unit to execute the conversion, the element from the shape database storing various shape data of the elements constituting the frequency selection plate An element shape data selection command for selecting shape data and a command for performing optimization processing of the frequency selection plate using the selected element shape data as a parameter are provided.

  In the invention according to claim 6, in the optimization program according to claim 5, the command for performing the optimization process of the frequency selection plate is a command using the unit cell size ratio of the frequency selection plate as a parameter. It is characterized by being.

  By using the optimization system, the optimization method and the optimization program for the object incorporating the multilayer frequency selective plate according to the present invention, the convergence is better than the conventional one and the calculation processing time for the optimization is reduced. can do. Moreover, the frequency selection board of the shape which can be manufactured easily can be obtained.

  DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of an optimization system for a multilayer frequency selective plate built-in object, an optimization method thereof, an optimization program thereof, and a recording medium according to the present invention will be described in detail with reference to the drawings. The present invention is not limited to the embodiments.

  A first embodiment of the system for optimizing a multilayer frequency selective plate built-in object according to the present invention will be described with reference to FIGS. In the first embodiment, a method using a genetic algorithm is exemplified as an optimization method.

  First, the configuration of the optimization system according to the first embodiment will be described with reference to FIG.

As shown in FIG. 1, the optimization system includes an input means 1 such as a keyboard and a mouse for an operator to input design specification information, etc., to be described later of an object incorporating a multilayer frequency selection board, and the multilayer frequency selection board incorporated. the optimization processing unit 2 for performing an optimization process of an object, data of various shapes FSS element made of metal wire or the like for constituting a frequency selective plate (FSS) (hereinafter referred to as "element shape data".) e _n (N = 1, 2, 3,...) In which the shape database 3 is stored, the output means 4 such as a monitor for displaying the processing results and the like, and the multilayer frequency selection plate for the optimum solution obtained by the optimization processing means 2 It comprises frequency selection plate analysis means 5 for performing performance analysis of an embedded object.

  First, the optimization processing means 2 is connected to a control unit 2A such as a CPU (central processing unit) for controlling optimization processing according to the present invention, and the input means 1 and output means 4 as shown in FIG. In addition, an input / output interface unit 2B that performs respective controls and a main storage unit 2C are provided, and these units 2A, 2B, and 2C are communicably connected via an arbitrary communication path. The control unit 2A is communicably connected to the shape database 3 via any wired or wireless communication path.

  Here, the control unit 2A of the optimization processing means 2 specifically includes an element shape data selection unit 2a, a coding processing unit 2b, an fitness function calculation unit 2c, a parent element selection processing unit 2d, shown in FIG. A crossover processing unit 2e, a mutation processing unit 2f, and a convergence condition determination processing unit 2g are provided.

The element shape data selecting section 2a, when the input settings from the operator which will be described later is performed, the element shape from the shape database 3 data _{e n (n = 1,2,3, ...} ) is selected, the coding processing unit 2b A processing function for passing the processing to

Next, coding processing section 2b is to create multiple parameter group designed serving multilayer frequency selective surface assembly write object based on the element shape data e _n selected by the element shape data selecting section 2a, of the parameter group Each of the parameters is encoded into a binary code to have a processing function for generating an initial population.

For example, when the multilayer frequency selection plate incorporated object to be designed is one in which the frequency selection plates F1 and F2 are incorporated between the three dielectrics D1 to D3 shown in FIG. processing unit 2b, the element shape data _{e n (n = 1,2,3, ...} ) of each frequency selective surface F1, F2 and selected, the thickness t ₁ of the dielectric D1 to D3, t _2, t ₃ and a plurality of sets of multi-layer frequency selection plates that are binary coded as shown in FIG. 3, using the unit cell size ratios r ₁ and r _{2 of} the frequency selection plate F1 and the frequency selection plate F2 as parameters. Parameter groups (t ₁ , e ₁ , r ₁ , t ₂ , e ₂ , r ₂ , t ₃ ) are created.

The unit cell size ratio r of the frequency selection plate is r = 1: N in the case of a frequency selection plate made up of N × N cells, and a plurality of types are set in advance. Here, when the unit cell size ratio r ₁ = 1: 2 of the frequency selection plate F1 and the unit cell size ratio r ₂ = 1: 3 of the frequency selection plate F2, the common multiples “5, 10,. It is preferable to obtain an optimum solution of the frequency selection plates F1 and F2 having a width of “ That is, when each of the frequency selection plates F1 and F2 is 30 mm × 30 mm, the frequency selection plate F1 is composed of 2 × 2 cells and has unit cells of 15 mm × 15 mm, The selection plate F2 is preferably composed of 3 × 3 cells and has 10 mm × 10 mm unit cells. Thereby, it becomes possible to facilitate the analysis processing of the frequency selection plate analyzing means 5 described later.

In the case of optimizing the multilayer frequency selection plate built-in object, the dielectric constants of the dielectrics D1 to D3 may be used as parameters, but in the first embodiment, the explanation is simplified. For this purpose, only the thicknesses t ₁ , t ₂ , and t ₃ will be described as parameters of the dielectrics D1 to D3.

  The fitness function calculation unit 2c is based on, for example, design specification conditions (power transmittance, power reflectance, frequency of transmitted or reflected radio waves, incident angle of radio waves, etc.) input and set by an operator as described later. A processing function for defining the fitness function, and a processing function for evaluating the fitness of the initial group and child elements described later using the fitness function.

  Further, the parent element selection processing unit 2d has a processing function of selecting a parent element having a high evaluation based on the evaluation result of the fitness function calculation unit 2c. The crossover processing unit 2e has a function of performing single point crossover or multipoint crossover processing of the genetic algorithm from its parent element, and the mutation processing unit 2f is a function of performing mutation processing of the genetic algorithm. A child element is generated from the parent element by the processing function.

  Further, the convergence condition determination processing unit 2g has a processing function for determining whether or not the child elements have converged using a convergence condition input and set by an operator as will be described later. Here, the convergence condition is a condition such as “until a certain generation (for example, up to 1000 generations)”, “until a predetermined performance level”, and the like, and when the child element satisfies such a condition, This is a condition for ending the optimization process assuming that an optimal solution has been obtained.

  The various processing functions of the control unit 2A as described above are realized by an optimization processing program that controls the CPU, the input / output interface unit 2B, the storage device such as the shape database 3, the memory device, the input unit 1, the output unit 4, and the like. Is done. For example, the optimization processing program includes an element shape data selection command, a coding processing command, a fitness function calculation command, a parent element selection command, a crossover processing command, a mutation, which causes the CPU to perform the calculation processing of each of the units 2a to 2g. A processing command and a convergence condition determination command are provided. The optimization processing program is stored in an optical recording medium such as a CD-ROM, for example, and is read by a reading device (not shown) and stored in a magnetic recording medium such as an HDD to execute the optimization processing.

Next, the shape database 3 will be described. The shape database, the element shape data e _n in various shapes as described above (n = 1,2,3, ...) is a database that is stored. For example, in the present embodiment 1, the element shapes of a plurality of patch type as shown in FIG. 4 the data _{e n (n = 1,2,3, ...} ) is stored. Here, the shape database 3 stores element shape data that is known in advance to exhibit frequency selection performance. Moreover, when storing, it is preferable to store the thing of a shape with easy manufacture after considering the productivity of a frequency selection board.

The element shape data e _n stored in this shape database 3 is not intended to be limited to the shape as shown in FIG. 4, it may be an element shape data of patch other shape, It may be a slot type. Further, not limited to those made of one shape may be a combination of two or more shapes in element shape data e _n.

  The frequency selection plate analyzing means 5 performs the performance analysis of the multilayer frequency selection plate built-in object having the optimum solution obtained by the optimization processing means 2 as described above. A moment method (MoM), a time domain difference method (FDTD method), or a finite element method (FEM) can be used.

  Next, the processing operation of this optimization system will be described based on the flowchart of FIG. Here, as an example of the multilayer frequency selection plate built-in object, a radio wave absorber in which frequency selection plates F1 and F2 are interposed between the three dielectrics D1 to D3 shown in FIG. 2 will be described as an example. To do.

  First, an operator inputs design settings by inputting design specification information of a radio wave absorber to be designed from the input means 1 (step ST1).

For example, as the design specification information input setting of the first embodiment, the thicknesses t ₁ , t ₂ , and t ₃ of the dielectrics D1 to D3 are set. In addition, setting of design specification conditions such as power transmittance, power reflectance, frequency of radio waves to be transmitted or reflected or incident angle of radio waves, “up to a certain generation (for example, up to 1000 generations)”, “predetermined performance level” Set the convergence condition such as “until”. Here, the input setting is performed in accordance with the input setting screen displayed on the output means 4, and the input content is stored in the main storage unit 2C.

  Next, the optimization processing means 2 randomly generates a plurality of radio wave absorber evaluation models (initial population) using a genetic algorithm (step ST2).

Optimization processing means 2 of the specific embodiment 1, as shown in the flowchart of FIG. 6, to select the element shape data e _n of the frequency selective surface F1, F2 from the shape database 3 by the element shape data selecting section 2a (Step ST2A). Then, the coding processing unit 2b, the element shape data e _n and the selection, the thickness t ₁ of the dielectric D1 to D3, t _2, t _3, the unit cell of each of the frequency selective surface F1 and the frequency selective surface F2 The size ratio r ₁ , r ₂ is used as a parameter (step ST2B), and a plurality of sets of radio wave absorbers {for example, in the case of element shape data e ₁ , e ₂ , parameter groups (t ₁ , e ₁ , r ₁ , t ₂ , e ₂ , r ₂ , t ₃ )} (step ST2C).

Here, in the first embodiment, the unit cell is divided into 32 (= 2 ⁵ ) ranges of the thicknesses t ₁ , t ₂ , and t ₃ of the dielectrics D1 to D3 set in step ST1. ratio r ₁ of size, r ₂ is 4 (= 2 ²⁾ is preset to the type, it is assumed that the shape data e _n in shape DB is n = 32 (= 2 ⁵⁾ types available. For this reason, the optimization processing means 2 assigns 5 bits to the thicknesses t ₁ , t ₂ , and t ₃ of the dielectrics D1 to D3 by the coding processing unit 2b, and sets ₂ to the unit cell size ratios r ₁ and r ₂ . assign a bit by assigning 5 bits to form data e _n, the parameter group of a plurality of sets of the radio wave absorber {e.g. parameter groups _{(t 1, e 1, r} 1, t 2, e 2, r 2, t 3 )} Is binary-coded as shown in FIG. 3, for example (step ST2D).

In this way, the optimization processing means 2 can use a plurality of sets of binary-coded parameter absorbers {for example, parameter groups (t ₁ , e ₁ , r ₁ , t ₂ , e ₂ , r ₂ , t ₃ )} is created and stored in the main memory 2C as an initial group.

  Next, the optimization processing means 2 of the first embodiment performs an optimization process using a known genetic algorithm.

  That is, the optimization processing means 2 defines the fitness function from the design specification condition, for example, by the fitness function calculation unit 2c (step ST3), and uses this fitness function for each individual of the initial population. The fitness is calculated (step ST4). As a result, the optimization processing means 2 selects a parent element having a high evaluation by the parent element selection processing unit 2d (step ST5), and thereafter, this parent element is selected by the crossover processing unit 2e and the mutation processing unit 2f. Are subjected to operations such as single-point crossover or multipoint crossover, and further mutation, to generate child elements (steps ST6 and ST7).

  Subsequently, the optimization processing means 2 determines the child element based on the convergence condition set in step ST1 (step ST8).

  If the convergence condition has not been reached, the process returns to step ST3, and the above process is repeated with the current child element as the next generation parent element. If the convergence condition is satisfied in step ST7, the optimization process is terminated with the child element as an optimal solution, and the process is passed to the frequency selection plate analysis means 5.

In this frequency selection plate analyzing means 5, the performance analysis of the multilayer frequency selection plate built-in object of the optimum solution is performed (step ST9). If the desired performance is not satisfied, the process returns to step ST2 and a new element is obtained. select the shape data e _n from the shape database 3, it repeats the same process. If the desired performance is satisfied in step ST, all the processes are terminated. The result is displayed on the output means 4.

By obtaining an optimum solution with as indicated in the embodiment 1 or more, i.e., advance performance prepared database in which a plurality of shape data e _n is registered that has become apparent, and its shape data e _n to the parameter Since the optimization process is performed, the convergence is improved, and the calculation processing time required for the optimization process can be reduced.

Specifically, when designing a multilayer frequency selection plate built-in object in which a plurality of frequency selection plates made up of 16 × 16 cells, for example, is designed, conventionally, 16 frequency selection plates F1 are optimized. X16 = 256 bits were required. However, the optimization system of the first embodiment, the registration number 32 of the shape data e _n shape DB described above, when the ratio r ₁ of the unit cell size of the frequency selective plate F1 is four, a single frequency In order to optimize the selection board F1, 5 bits (32 = 2 ⁵ ) +2 bits (4 = 2 ² ) = 7 bits are sufficient. For this reason, the optimization system of the first embodiment has better convergence than the conventional system, and can reduce the optimization processing time. In order to achieve such an effect, the unit cell size ratios r ₁ and r ₂ of the frequency selection plates F1 and F2 are not necessarily used as parameters.

  In addition, the optimization system of the first embodiment can optimize the layers of the frequency selection plates having different unit cell sizes, and can obtain frequency selection plates that are easy to manufacture in a simple shape (not likely to cause defects). it can. In addition, this makes it possible not only to reduce the manufacturing cost but also to construct a high-performance multilayer frequency selective plate built-in object (radio wave absorber).

[Embodiment 2] A second embodiment of the system for optimizing a multilayer frequency selective plate built-in object according to the present invention will be described. Optimization system of this embodiment 2 is made of a similar configuration and optimization system of the first embodiment described above, the difference is in the choice of the element shape data e _n by the element shape data selecting section 2a .

Here, by changing the element shape for each layer of the frequency selection plate, a multilayer frequency selection plate built-in object having a plurality of types of frequency characteristics can be obtained. In addition, by providing a plurality of types of FSS elements on one frequency selection plate, and further changing the ratio of each of the frequency selection plates in one frequency selection plate even if the FSS elements have the same shape, A multilayer frequency selective plate built-in object having a plurality of types of frequency characteristics can be obtained. So, was exemplified element shape data e _n one type In the optimization system of Example 1 as being selected from the shape database 3, two kinds of element shape data e is the optimization system of the present embodiment 2 select _n, or will be exemplified a case of changing the element shape data e _n select and respective proportions of the same shape.

  In this case, first, in step ST1 of the flowchart shown in FIG. In step ST2, an initial group is generated.

In the second embodiment, when generating the initial group, two (two types or two of the same shape) elements are selected by the element shape data selection unit 2a in step ST2A of the flowchart shown in FIG. selecting the shape data e _n. Then, in step ST2B～ST2D, coding processing unit 2b, two element shape data e _n selected, the thickness t ₁ of the dielectric D1 to D3, t _2, t _3, the frequency selective surface F1 and the frequency selective surface unit cell size ratio of each of the F2 r _1, r _2, the dimensional ratio r _e of the element shape data e _n as a parameter, to create an initial population serving binary coded sets of parameter groups.

For example, when the frequency selection plate F1 is two types of shape data e ₃ and e ₅ and the frequency selection plate F2 is two types of shape data e ₁ and e ₄ , a plurality of sets of parameter groups (t _{1, e 3, e 5,} r 1, t 2, e 1, e 4, r 2, t 3, r e) is created. Further, the frequency selective surface F1 is the shape data e ₃ of the same shape two, when the frequency selective surface F2 is the shape data e ₅ of the same shape of the two are binary coded sets of parameter groups (t _{1, e 3, e 3,} r 1, t 2, e 5, e 5, r 2, t 3, r e) is created.

  Thereafter, optimization processing and analysis processing using a genetic algorithm are performed in the same manner as steps ST3 to ST9 of the first embodiment.

For example, the frequency selection plate F1 thus obtained has shape data e ₃ and e ₅ as shown in FIG.

  Even if the optimum solution is obtained as in the second embodiment, as in the first embodiment described above, the effect of reducing the calculation processing time by improving the convergence is obtained, and furthermore, a frequency selection plate that is easy to manufacture is obtained. Can do. In addition, by providing a plurality of types or the same type of FSS elements having different size ratios on one frequency selection plate, the number of dielectric layers and frequency selection plate layers can be reduced. It is also possible to reduce the thickness and thickness of the multilayer frequency selection plate built-in object.

  In each of the first and second embodiments described above, the genetic algorithm is exemplified as an example of the optimization method. However, the optimization method is not necessarily limited to this, for example, the annealing method or the like. May be used.

  As described above, the optimization system, the optimization method and the optimization program for the multilayer frequency selective plate built-in object according to the present invention are designed to simplify the design and manufacture of the multilayer frequency selective plate built-in object, Useful for reduction.

It is a block diagram which shows an example of a structure of the optimization system which concerns on this invention. It is a figure which shows an example of the multilayer frequency selection board built-in object optimized with the optimization system which concerns on this invention. It is a figure which shows an example of the parameter group produced with the optimization system which concerns on this invention. It is a figure which shows an example of the element shape data stored in the shape database of the optimization system which concerns on this invention. It is a flowchart explaining the processing operation of the optimization system which concerns on this invention. It is a flowchart explaining the initial group generation process operation | movement of the optimization system which concerns on this invention. It is a figure which shows an example of the frequency selection board obtained in Example 2 of the optimization system which concerns on this invention.

Explanation of symbols

2 optimization processing means 2A control unit 2a element shape data selection unit 2b coding processing unit 2c fitness function calculation unit 2d parent element selection processing unit 2e crossover processing unit 2f mutation processing unit 2g convergence condition determination processing unit 3 shape database F1, F2 frequency selective surface _{e n (n = 1,2,3, ...} ) element shape data r _1, r ₂ unit cell size ratio

Claims (6)

A system for optimizing an object incorporating a multi-layer frequency selection plate, which is a target of design, using a predetermined optimization technique such as a genetic algorithm or annealing method,
Optimization processing means for performing optimization processing of various parameters of the multilayer frequency selection plate built-in object by the optimization method, and a shape database storing data of various shapes of elements constituting the frequency selection plate Prepared,
The optimization processing means includes an element shape data selection unit that selects element shape data from the shape database, and a processing unit that performs optimization processing of the frequency selection plate using the selected element shape data as a parameter. A system for optimizing a built-in object of a multilayer frequency selective plate characterized by that.   The multilayer processing unit according to claim 1, wherein the processing unit uses a unit cell size ratio of the frequency selection plate as a parameter in order to optimize the frequency selection plate. Optimization system. A method for optimizing an object incorporating a multi-layer frequency selection board, in which optimization of an object incorporating a multi-layer frequency selection board to be designed is performed using a predetermined optimization method such as a genetic algorithm or annealing method,
Selecting the element shape data from a shape database storing data of various shapes of elements constituting the frequency selection plate, and performing the optimization process of the frequency selection plate using the selected element shape data as a parameter A method for optimizing a multilayer frequency selective plate built-in object.   4. The method of optimizing a multilayer frequency selection plate built-in object according to claim 3, wherein when performing the optimization processing of the frequency selection plate, a unit cell size ratio of the frequency selection plate is also used as a parameter. Multi-layer frequency selection board with various commands that allow the central processing unit to execute optimization of an object incorporating a multi-layer frequency selection board to be designed using a predetermined optimization method such as genetic algorithm or annealing method An embedded object optimization program,
An element shape data selection command for selecting the element shape data from a shape database storing data of various shapes of elements constituting the frequency selection plate, and the selected element shape data as parameters. A program for optimizing a multi-layer frequency selection board built-in object, comprising an instruction for performing an optimization process.   6. The program for optimizing a multilayer frequency selection plate built-in object according to claim 5, wherein the command for optimizing the frequency selection plate is a command using a unit cell size ratio of the frequency selection plate as a parameter.

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