CN109635420B - A simulation method and system of a microwave microstrip hairpin filter - Google Patents

A simulation method and system of a microwave microstrip hairpin filter Download PDF

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CN109635420B
CN109635420B CN201811500423.4A CN201811500423A CN109635420B CN 109635420 B CN109635420 B CN 109635420B CN 201811500423 A CN201811500423 A CN 201811500423A CN 109635420 B CN109635420 B CN 109635420B
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季鲁
张金利
朱添羽
何�泽
何明
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Abstract

The invention discloses a simulation method and a system of a microwave microstrip hairpin filter, wherein the method comprises the following steps: acquiring simulation data of the accumulated microwave microstrip filter; establishing a prediction model based on a BP neural network; training the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain a trained prediction model; inputting the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain the microwave microstrip filter for predicting the scattering parameters; judging whether the microwave microstrip filter for predicting the scattering parameters meets simulation requirements or not, and if so, outputting the microwave microstrip filter for predicting the scattering parameters; if not, returning to the step of acquiring the simulation data of the accumulated microwave microstrip filter. The invention realizes the automatic simulation of the microwave microstrip hairpin filter and reduces manpower and material resources.

Description

Simulation method and system of microwave microstrip hairpin type filter
Technical Field
The invention relates to the technical field of filter design, in particular to a simulation method and a simulation system of a microwave microstrip hairpin-type filter.
Background
The microwave microstrip filter is an important device in modern communication systems and plays a role in screening out irrelevant signals. The traditional microwave microstrip filter simulation method including single-port group delay, a space mapping method and the like needs a designer to carry out design and simulation operations before a computer from beginning to end, needs a certain simulation experience, is time-consuming and labor-consuming, has low automation degree, has design efficiency which is changed according to the level of the worker, and is difficult to be competent for a large amount of design work of the filter.
Disclosure of Invention
The invention aims to provide a simulation method and a simulation system for a microwave microstrip hairpin filter, so as to realize automatic simulation of the microwave microstrip hairpin filter and reduce manpower and material resources.
In order to achieve the above object, the present invention provides a method for simulating a microstrip hairpin filter, the method comprising:
acquiring simulation data of the accumulated microwave microstrip filter; the accumulated simulation data of the microwave microstrip filter is that a large amount of simulation is automatically completed by controlling electromagnetic simulation software through MATLAB, and all the simulation data of the microwave microstrip filter of the filter are returned;
establishing a prediction model based on a BP neural network;
training the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain a trained prediction model;
inputting the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain the microwave microstrip filter for predicting the scattering parameters;
judging whether the microwave microstrip filter for predicting the scattering parameters meets simulation requirements or not, and if so, outputting the microwave microstrip filter for predicting the scattering parameters; if not, returning to the step of acquiring the simulation data of the accumulated microwave microstrip filter.
Optionally, the acquiring simulation data of the accumulated microwave microstrip filter specifically includes:
adjusting the size parameters of each strip in the microwave microstrip filter according to preset frequency in MATLAB control electromagnetic simulation software to ensure that the whole size of the microwave microstrip filter is fixed and unchanged, and obtaining a plurality of groups of microwave microstrip filters with scattering parameters; wherein the dimensional parameters include a length and a width of the strip.
Optionally, the establishing a prediction model based on a BP neural network specifically includes:
setting the number of elements of an input layer and the number of elements of an output layer;
calculating the number of hidden layers according to the number of the input layer elements and the number of the output layer elements;
establishing a prediction model by taking a sigmoid function as an activation function;
optionally, the training of the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain the trained prediction model specifically includes:
inputting the accumulated microwave microstrip filter simulation data into an input layer of the prediction model;
determining the connection weight of the input layer and the hidden layer and the connection weight of the hidden layer and the output layer by using a random gradient descent method;
determining hidden layer input data through input data of an input layer and connection weight of the input layer and the hidden layer;
inputting the hidden layer input data into an activation function to obtain hidden layer output data;
determining output layer input data according to the hidden layer output data and the connection weight of the hidden layer and the output layer;
inputting the input data of the output layer into the activation function for reverse processing, and outputting the data;
and transmitting the error between the output data and the target output data to the hidden layer as a feedback signal, and adjusting the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer in the hidden layer until the error is within a set threshold range to obtain a trained prediction model.
Optionally, the determining whether the microwave microstrip filter for predicting the scattering parameter meets the simulation requirement specifically includes:
judging whether the error between the predicted scattering parameter of the microwave microstrip filter of the predicted scattering parameter and the target scattering parameter is smaller than a set threshold value, if so, meeting the simulation requirement; if not, the simulation requirement is not met.
The invention also provides a simulation system of the microwave microstrip hairpin-type filter, which comprises:
the data acquisition unit is used for acquiring the accumulated microwave microstrip filter simulation data; the accumulated simulation data of the microwave microstrip filter is that a large amount of simulation is automatically completed by controlling electromagnetic simulation software through MATLAB, and all the simulation data of the microwave microstrip filter of the filter are returned;
the model establishing unit is used for establishing a prediction model based on a BP neural network;
the training unit is used for training the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain a trained prediction model;
the prediction unit is used for inputting the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain the microwave microstrip filter with the predicted scattering parameters;
the judging unit is used for judging whether the microwave microstrip filter for predicting the scattering parameters meets the simulation requirements or not, and if so, outputting the microwave microstrip filter for predicting the scattering parameters; if not, returning to the step of acquiring the simulation data of the accumulated microwave microstrip filter.
Optionally, the data obtaining unit specifically includes:
the size adjusting subunit is used for adjusting the size parameters of each strip in the microwave microstrip filter according to the preset frequency in MATLAB control electromagnetic simulation software, so that the overall size of the microwave microstrip filter is fixed and unchanged, and the microwave microstrip filter with multiple groups of scattering parameters is obtained; wherein the dimensional parameters include a length and a width of the strip.
Optionally, the model establishing unit specifically includes:
the setting subunit is used for setting the number of elements of the input layer and the number of elements of the output layer;
a hidden layer number calculating subunit, configured to calculate the number of hidden layers according to the number of input layer elements and the number of output layer elements;
the device comprises a building subunit, a prediction unit and a prediction unit, wherein the building subunit is used for building a prediction model by taking a sigmoid function as an activation function;
optionally, the training unit specifically includes:
an input subunit, configured to input the accumulated microwave microstrip filter simulation data into an input layer of the prediction model;
the weight determining subunit is used for determining the connection weight of the input layer and the hidden layer and the connection weight of the hidden layer and the output layer by using a random gradient descent method;
the hidden layer input data determining subunit is used for determining hidden layer input data through input data of an input layer and the connection weight of the input layer and the hidden layer;
the hidden layer output data determining subunit is used for inputting the hidden layer input data into an activation function to obtain hidden layer output data;
the output layer input data determining subunit is used for determining output layer input data according to the hidden layer output data and the connection weight of the hidden layer and the output layer;
the reverse processing subunit is used for inputting the input data of the output layer into the activation function for reverse processing and then outputting the data;
and the weight adjusting subunit is used for transmitting the error between the output data and the target output data to the hidden layer as a feedback signal, adjusting the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer in the hidden layer until the error is within a set threshold range, and obtaining the trained prediction model.
Optionally, the determining unit specifically includes:
the parameter judgment subunit is used for judging whether the error between the predicted scattering parameter of the microwave microstrip filter of the predicted scattering parameter and the target scattering parameter is smaller than a set threshold value, and if so, the simulation requirement is met; if not, the simulation requirement is not met.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
1. the filter design and simulation are combined with the machine learning, so that the design and simulation of the filter can be automatically completed by a computer by utilizing the machine learning, and the design efficiency is greatly improved.
2. From low order filter design to high order filter design, this can be done by machine learning. In the conventional filter design, the higher the filter order is, the higher the labor cost is, and the more obvious the advantage of machine learning in the high-order filter design is.
3. The design of high frequency filters, e.g. 8GHz to 30GHz, is more complex than low frequency filters. The method can be popularized and applied to high-frequency filters, even terahertz frequency band filters (100 GHz).
In summary, the simulation method and system for the microstrip hairpin-type filter provided by the invention apply artificial intelligence based on machine learning to the filter design and optimization, for a computer, the whole process can be completely and automatically completed, the improvement of the order only means the increase of the output parameters, the design difficulty existing in manual design does not exist, and the design of the filter with any order can be completed only with enough calculation power theoretically, which means that the invention greatly saves manpower and improves the design efficiency. The design cost is reduced. In addition, aiming at the filter with a complex configuration, the invention can complete the design and simulation work which is hard to be competed by a designer.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a simulation method of a microstrip hairpin filter according to an embodiment of the present invention;
fig. 2 is a system block diagram of a simulation system of a microstrip hairpin filter according to an embodiment of the present invention;
FIG. 3 is a block diagram of a first order filter obtained by simulation using the present invention;
FIG. 4 is a graph of a first order filter target scattering parameter to be simulated;
FIG. 5 is a graph comparing predicted scattering parameters and target scattering parameters of a first order filter obtained by simulation according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the simulation method of the microstrip hairpin filter provided in this embodiment includes:
step 101: acquiring simulation data of the accumulated microwave microstrip filter; the accumulated simulation data of the microwave microstrip filter is that a large amount of simulation is automatically completed by controlling electromagnetic simulation software through MATLAB, and all the simulation data of the microwave microstrip filter of the filter are returned.
Specifically, the step 101 specifically includes:
adjusting the size parameters of each strip in the microwave microstrip filter according to preset frequency in MATLAB control electromagnetic simulation software to ensure that the whole size of the microwave microstrip filter is fixed and unchanged, and obtaining a plurality of groups of microwave microstrip filters with scattering parameters; wherein the dimensional parameters include a length and a width of the strip.
That is, before simulation, the MATLAB controls the electromagnetic simulation software to edit codes, and then the MATLAB controls the electromagnetic simulation software to run to automatically realize the automatic design of the microwave microstrip filter, thereby acquiring a large amount of accumulated simulation data of all the microwave microstrip filters. In the simulation, the changed parameters are the physical dimensions of each strip (see the black strip structure in fig. 3) in the layout of the microwave microstrip filter, that is, the length and width (fixed thickness) of each strip, the changed frequency range is 3-8GHz (preset frequency), the size of the whole size of the filter is fixed, and finally, a large amount of data of different physical dimensions of the filter and corresponding different S parameters (scattering parameters) are obtained.
Step 102: establishing a prediction model based on a BP neural network;
the calculation process of the BP neural network consists of a forward calculation process and a backward calculation process. And in the forward propagation process, the input mode is processed layer by layer from the input layer through the hidden unit layer and is transferred to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. If the expected output can not be obtained at the output layer, the reverse propagation is carried out, the error signal is returned along the original connecting path, and the weight of each neuron is modified to minimize the error signal.
Forward propagation: the input samples are processed from the input layer through the hidden layer by layer, and are transmitted to the output layer after passing through all the hidden layers; during the layer-by-layer process, the state of each layer of neurons only affects the state of the next layer of neurons. The present output is compared with the expected output at the output layer and if the present output is not equal to the expected output, the back propagation process is entered.
And (3) back propagation: the error signal is transmitted back according to the original forward propagation path, and the weight coefficient of each neuron of each hidden layer is modified so as to expect the error signal to tend to be minimum.
The step 102 specifically includes:
setting the number of elements of an input layer and the number of elements of an output layer;
calculating the number of hidden layers according to the number of the input layer elements and the number of the output layer elements;
and establishing a prediction model by taking the sigmoid function as an activation function.
In this embodiment, a total of 4 hidden layers are used in the training process, each layer including 20 neurons. The BP neural network is a neural network with data forward propagation and error backward propagation, in the training process, a sigmoid function is adopted as an activation function, the number of input layer elements x of the neural network is 501, the number of output layer elements y of the neural network is 4, and the BP neural network is based on a formula
Figure BDA0001898016810000071
The number of hidden layers n can be calculated, where a is an integer between 1 and 10.
Step 103: and training the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain the trained prediction model.
The step 103: the method specifically comprises the following steps:
inputting the accumulated microwave microstrip filter simulation data into an input layer of the prediction model;
determining the connection weight of the input layer and the hidden layer and the connection weight of the hidden layer and the output layer by using a random gradient descent method;
determining hidden layer input data through input data of an input layer and connection weight of the input layer and the hidden layer;
inputting the hidden layer input data into an activation function to obtain hidden layer output data;
determining output layer input data according to the hidden layer output data and the connection weight of the hidden layer and the output layer;
inputting the input data of the output layer into the activation function for reverse processing, and outputting the data;
and transmitting the error between the output data and the target output data to the hidden layer as a feedback signal, and adjusting the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer in the hidden layer until the error is within a set threshold range to obtain a trained prediction model.
During the training process, the loss function used is:
Figure BDA0001898016810000072
each training sample is weighted toward its negative gradient by using a stochastic gradient descent method, and the gradient of L to the connecting weight w is
Figure BDA0001898016810000073
Calculating the derivative of L to the input s as
Figure BDA0001898016810000081
Is marked as
Figure BDA0001898016810000082
Then the hidden layer δ is
Figure BDA0001898016810000083
Output layer δ is
Figure BDA0001898016810000084
Wherein y represents an output; s represents an input; l represents a loss function; SSE denotes the sum of the residual squares; SST denotes the sum of the squares; SSR represents regression sum of squares;
Figure BDA0001898016810000085
represents the square of the mathematical expectation for the jth element of the output layer y; e.g. of the typeiRepresents the mathematical expectation for the ith element of the output layer y;
Figure BDA0001898016810000086
represents the average of the jth element of the output layer; y isjCurrent value representing jth element of output layer; x is the number ofiAn ith element representing an input;
Figure BDA0001898016810000087
an input representing a jth node of the hidden layer;
Figure BDA0001898016810000088
an input representing an ith node of the output layer;
Figure BDA0001898016810000089
representing the connection weight from the jth node of the hidden layer to the ith node of the output layer; θ' () represents the value of the coefficient derivative in parentheses;
Figure BDA00018980168100000810
representing the connection weight from the ith node of the input layer to the jth node of the hidden layer;
Figure BDA00018980168100000811
error representing the ith input of the hidden layer;
Figure BDA00018980168100000812
error representing the jth output of the hidden layer;
Figure BDA00018980168100000813
representing the error of the ith input of the output layer.
After the training model is obtained through the steps, the prediction capability of the training model, namely the accuracy of the automatic design and simulation filter can be evaluated according to the following formula:
Figure BDA00018980168100000814
wherein SST is the sum of the squares of the total squares, SSR is the sum of the squares of the regression, and SSE is the sum of the squares of the residuals. Calculated R2A closer to 1 indicates a better fit. After the expected requirements are met, the model can be used for automatic design and simulation of the filter.
Step 104: and inputting the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain the microwave microstrip filter with the predicted scattering parameters.
Step 105: judging whether the microwave microstrip filter for predicting the scattering parameters meets simulation requirements or not, and if so, outputting the microwave microstrip filter for predicting the scattering parameters; if not, returning to the step of acquiring the simulation data of the accumulated microwave microstrip filter.
The step 105 specifically includes:
judging whether the error between the predicted scattering parameter of the microwave microstrip filter of the predicted scattering parameter and the target scattering parameter is smaller than a set threshold value, if so, meeting the simulation requirement; if not, the simulation requirement is not met.
The present embodiment provides a system corresponding to the simulation method for a microstrip hairpin filter provided in the foregoing embodiment, where the system includes:
a data obtaining unit 201, configured to obtain simulation data of the accumulated microwave microstrip filter; the accumulated simulation data of the microwave microstrip filter is that a large amount of simulation is automatically completed by controlling electromagnetic simulation software through MATLAB, and all the simulation data of the microwave microstrip filter of the filter are returned;
a model establishing unit 202, configured to establish a prediction model based on a BP neural network;
the training unit 203 is configured to train the prediction model by using the accumulated microwave microstrip filter simulation data as training data to obtain a trained prediction model;
the prediction unit 204 is configured to input the target scattering parameter of the microwave microstrip filter to the trained prediction model to obtain a microwave microstrip filter with a predicted scattering parameter;
a judging unit 205, configured to judge whether the microwave microstrip filter for predicting the scattering parameter meets the simulation requirement, and if so, output the microwave microstrip filter for predicting the scattering parameter; if not, returning to the step of acquiring the simulation data of the accumulated microwave microstrip filter.
The data obtaining unit 201 specifically includes:
the size adjusting subunit is used for adjusting the size parameters of each strip in the microwave microstrip filter according to the preset frequency in MATLAB control electromagnetic simulation software, so that the overall size of the microwave microstrip filter is fixed and unchanged, and the microwave microstrip filter with multiple groups of scattering parameters is obtained; wherein the dimensional parameters include a length and a width of the strip.
The model establishing unit 202 specifically includes:
the setting subunit is used for setting the number of elements of the input layer and the number of elements of the output layer;
a hidden layer number calculating subunit, configured to calculate the number of hidden layers according to the number of input layer elements and the number of output layer elements;
the device comprises a building subunit, a prediction unit and a prediction unit, wherein the building subunit is used for building a prediction model by taking a sigmoid function as an activation function;
the training unit 203 specifically includes:
an input subunit, configured to input the accumulated microwave microstrip filter simulation data into an input layer of the prediction model;
the weight determining subunit is used for determining the connection weight of the input layer and the hidden layer and the connection weight of the hidden layer and the output layer by using a random gradient descent method;
the hidden layer input data determining subunit is used for determining hidden layer input data through input data of an input layer and the connection weight of the input layer and the hidden layer;
the hidden layer output data determining subunit is used for inputting the hidden layer input data into an activation function to obtain hidden layer output data;
the output layer input data determining subunit is used for determining output layer input data according to the hidden layer output data and the connection weight of the hidden layer and the output layer;
the reverse processing subunit is used for inputting the input data of the output layer into the activation function for reverse processing and then outputting the data;
and the weight adjusting subunit is used for transmitting the error between the output data and the target output data to the hidden layer as a feedback signal, adjusting the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer in the hidden layer until the error is within a set threshold range, and obtaining the trained prediction model.
The determining unit 205 specifically includes:
the parameter judgment subunit is used for judging whether the error between the predicted scattering parameter of the microwave microstrip filter of the predicted scattering parameter and the target scattering parameter is smaller than a set threshold value, and if so, the simulation requirement is met; if not, the simulation requirement is not met.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The practical effects of the present invention will be described below with reference to a specific embodiment:
fig. 3 shows a first order filter obtained by the method of the present invention. Fig. 4 is a graph of a first order filter target scattering parameter to be simulated. Fig. 5 is a comparison graph of the predicted scattering parameter and the target scattering parameter of the first-order filter obtained by simulation of the present invention, and it can be seen that the S parameter of the first-order filter is well matched with the target S parameter, which indicates that the accuracy of the prediction model obtained by the present invention can meet the actual use requirement, the center frequency of the filter is 6450MHz, the 3dB bandwidth is 35MHz, the used dielectric substrate is rogers RO4003C, the dielectric constant is 3.38, the tangent loss is 0.0038, and the thickness is 0.508 mm.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (6)

1.一种微波微带发卡型滤波器的仿真方法,其特征在于,所述方法包括:1. a simulation method of microwave microstrip hairpin filter, is characterized in that, described method comprises: 获取已累积微波微带滤波器仿真数据,具体包括:Obtain the accumulated microwave microstrip filter simulation data, including: 在MATLAB控制电磁仿真软件中按照预设频率调整微波微带滤波器中的各个条带的尺寸参数,使微波微带滤波器整体尺寸固定不变,得到多组散射参数的微波微带滤波器;其中,所述尺寸参数包括所述条带的长度和宽度;所述已累积微波微带滤波器仿真数据为通过MATLAB控制电磁仿真软件自动完成大量仿真,并返回滤波器的所有微波微带滤波器仿真数据;In the MATLAB control electromagnetic simulation software, the size parameters of each strip in the microwave microstrip filter are adjusted according to the preset frequency, so that the overall size of the microwave microstrip filter is fixed, and the microwave microstrip filter with multiple sets of scattering parameters is obtained; Wherein, the size parameters include the length and width of the strip; the accumulated microwave microstrip filter simulation data is to control the electromagnetic simulation software through MATLAB to automatically complete a large number of simulations, and return all the microwave microstrip filters of the filter simulation data; 建立基于BP神经网络的预测模型,具体包括:Establish a prediction model based on BP neural network, including: 设定输入层元素个数和输出层元素个数;Set the number of input layer elements and the number of output layer elements; 根据所述输入层元素个数和所述输出层元素个数计算隐含层个数;Calculate the number of hidden layers according to the number of elements in the input layer and the number of elements in the output layer; 以sigmoid函数为激活函数,建立预测模型;Use the sigmoid function as the activation function to establish a prediction model; 利用所述已累积微波微带滤波器仿真数据作为训练数据训练所述预测模型;Using the accumulated microwave microstrip filter simulation data as training data to train the prediction model; 将微波微带滤波器的目标散射参数输入至所述训练后的预测模型,得到预测散射参数的微波微带滤波器;inputting the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain a microwave microstrip filter for predicting the scattering parameters; 判断所述预测散射参数的微波微带滤波器是否满足仿真要求,若是,输出所述预测散射参数的微波微带滤波器;若否,返回步骤“获取已累积微波微带滤波器仿真数据”。It is judged whether the microwave microstrip filter of the predicted scattering parameter meets the simulation requirements, and if so, the microwave microstrip filter of the predicted scattering parameter is output; 2.根据权利要求1所述的微波微带发卡型滤波器的仿真方法,其特征在于,所述利用所述已累积微波微带滤波器仿真数据作为训练数据训练所述预测模型,得到训练后的预测模型,具体包括:2. the simulation method of microwave microstrip hairpin type filter according to claim 1, is characterized in that, described using described accumulated microwave microstrip filter simulation data as training data to train described prediction model, obtain after training forecasting models, including: 将所述已累积微波微带滤波器仿真数据输入所述预测模型的输入层;inputting the accumulated microwave microstrip filter simulation data into the input layer of the prediction model; 利用随机梯度下降法确定输入层与隐含层的连接权重和隐含层与输出层的连接权重;Use the stochastic gradient descent method to determine the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer; 通过输入层的输入数据和所述输入层与隐含层的连接权重确定隐含层输入数据;The input data of the hidden layer is determined by the input data of the input layer and the connection weight between the input layer and the hidden layer; 将所述隐含层输入数据输入激活函数中得到隐含层输出数据;Inputting the hidden layer input data into the activation function to obtain the hidden layer output data; 根据所述隐含层输出数据和所述隐含层与输出层的连接权重确定输出层输入数据;Determine the input data of the output layer according to the output data of the hidden layer and the connection weight between the hidden layer and the output layer; 将所述输出层输入数据输入至所述激活函数进行反向处理后,输出数据;After inputting the input data of the output layer into the activation function for reverse processing, output data; 以所述输出数据和目标输出数据之间的误差作为反馈信号传输至所述隐含层,调整所述隐含层中的所述输入层与隐含层的连接权重和所述隐含层与输出层的连接权重,直至所述误差在设定阈值范围内,得到训练后的预测模型。The error between the output data and the target output data is transmitted to the hidden layer as a feedback signal, and the connection weight between the input layer and the hidden layer in the hidden layer and the connection weight between the hidden layer and the hidden layer are adjusted. The connection weight of the output layer, until the error is within the set threshold range, to obtain the trained prediction model. 3.根据权利要求1所述的微波微带发卡型滤波器的仿真方法,其特征在于,所述判断所述预测散射参数的微波微带滤波器是否满足仿真要求,具体包括:3. The simulation method of microwave microstrip hairpin filter according to claim 1, is characterized in that, whether described judging whether the microwave microstrip filter of described predicted scattering parameter satisfies simulation requirement, specifically comprises: 判断所述预测散射参数的微波微带滤波器的预测散射参数与所述目标散射参数的误差是否小于设定阈值,若是,则满足仿真要求;若否,则不满足仿真要求。It is judged whether the error between the predicted scattering parameter of the microwave microstrip filter for predicting the scattering parameter and the target scattering parameter is less than the set threshold, if yes, the simulation requirement is satisfied; if not, the simulation requirement is not satisfied. 4.一种微波微带发卡型滤波器的仿真系统,其特征在于,所述系统包括:4. a simulation system of microwave microstrip hairpin filter, is characterized in that, described system comprises: 数据获取单元,用于获取已累积微波微带滤波器仿真数据,具体包括:The data acquisition unit is used to acquire the accumulated microwave microstrip filter simulation data, including: 尺寸调整子单元,用于在MATLAB控制电磁仿真软件中按照预设频率调整微波微带滤波器中的各个条带的尺寸参数,使微波微带滤波器整体尺寸固定不变,得到多组散射参数的微波微带滤波器;其中,所述尺寸参数包括所述条带的长度和宽度;所述已累积微波微带滤波器仿真数据为通过MATLAB控制电磁仿真软件自动完成大量仿真,并返回滤波器的所有微波微带滤波器仿真数据;The size adjustment subunit is used to adjust the size parameters of each strip in the microwave microstrip filter according to the preset frequency in the MATLAB-controlled electromagnetic simulation software, so that the overall size of the microwave microstrip filter is fixed, and multiple sets of scattering parameters are obtained. The microwave microstrip filter; wherein, the size parameters include the length and width of the strip; the accumulated microwave microstrip filter simulation data is to control the electromagnetic simulation software through MATLAB to automatically complete a large number of simulations, and return the filter All microwave microstrip filter simulation data of ; 模型建立单元,用于建立基于BP神经网络的预测模型,具体包括:The model establishment unit is used to establish a prediction model based on the BP neural network, including: 设定子单元,用于设定输入层元素个数和输出层元素个数;Set the subunit, which is used to set the number of elements in the input layer and the number of elements in the output layer; 隐含层个数计算子单元,用于根据所述输入层元素个数和所述输出层元素个数计算隐含层个数;a subunit for calculating the number of hidden layers, configured to calculate the number of hidden layers according to the number of elements in the input layer and the number of elements in the output layer; 建立子单元,用于以sigmoid函数为激活函数,建立预测模型;Establish a subunit for establishing a prediction model with the sigmoid function as the activation function; 训练单元,用于利用所述已累积微波微带滤波器仿真数据作为训练数据训练所述预测模型,得到训练后的预测模型;A training unit, used for using the accumulated microwave microstrip filter simulation data as training data to train the prediction model to obtain a trained prediction model; 预测单元,用于将微波微带滤波器的目标散射参数输入至所述训练后的预测模型,得到预测散射参数的微波微带滤波器;a prediction unit, configured to input the target scattering parameters of the microwave microstrip filter into the trained prediction model to obtain the microwave microstrip filter for predicting the scattering parameters; 判断单元,用于判断所述预测散射参数的微波微带滤波器是否满足仿真要求,若是,输出所述预测散射参数的微波微带滤波器;若否,调用数据获取单元。A judging unit for judging whether the microwave microstrip filter for predicting scattering parameters meets the simulation requirements, and if so, outputting the microwave microstrip filter for predicting scattering parameters; if not, calling a data acquisition unit. 5.根据权利要求4所述的微波微带发卡型滤波器的仿真系统,其特征在于,所述训练单元具体包括:5. the simulation system of microwave microstrip hairpin filter according to claim 4, is characterized in that, described training unit specifically comprises: 输入子单元,用于将所述已累积微波微带滤波器仿真数据输入所述预测模型的输入层;an input subunit for inputting the accumulated microwave microstrip filter simulation data into the input layer of the prediction model; 权重确定子单元,用于利用随机梯度下降法确定输入层与隐含层的连接权重和隐含层与输出层的连接权重;The weight determination subunit is used to determine the connection weight between the input layer and the hidden layer and the connection weight between the hidden layer and the output layer by using the stochastic gradient descent method; 隐含层输入数据确定子单元,用于通过输入层的输入数据和所述输入层与隐含层的连接权重确定隐含层输入数据;The hidden layer input data determination subunit is used for determining the input data of the hidden layer through the input data of the input layer and the connection weight between the input layer and the hidden layer; 隐含层输出数据确定子单元,用于将所述隐含层输入数据输入激活函数中得到隐含层输出数据;The hidden layer output data determination subunit is used for inputting the hidden layer input data into the activation function to obtain the hidden layer output data; 输出层输入数据确定子单元,用于根据所述隐含层输出数据和所述隐含层与输出层的连接权重确定输出层输入数据;an output layer input data determination subunit, configured to determine the output layer input data according to the hidden layer output data and the connection weight between the hidden layer and the output layer; 反向处理子单元,用于将所述输出层输入数据输入至所述激活函数进行反向处理后,输出数据;a reverse processing subunit, configured to input the output layer input data to the activation function for reverse processing, and then output the data; 权重调整子单元,用于以所述输出数据和目标输出数据之间的误差作为反馈信号传输至所述隐含层,调整所述隐含层中的所述输入层与隐含层的连接权重和所述隐含层与输出层的连接权重,直至所述误差在设定阈值范围内,得到训练后的预测模型。The weight adjustment subunit is used to transmit the error between the output data and the target output data as a feedback signal to the hidden layer, and adjust the connection weight between the input layer and the hidden layer in the hidden layer and the connection weight between the hidden layer and the output layer, until the error is within the set threshold range, and the trained prediction model is obtained. 6.根据权利要求4所述的微波微带发卡型滤波器的仿真系统,其特征在于,所述判断单元具体包括:6. The simulation system of microwave microstrip hairpin filter according to claim 4, is characterized in that, described judging unit specifically comprises: 参数判断子单元,用于判断所述预测散射参数的微波微带滤波器的预测散射参数与所述目标散射参数的误差是否小于设定阈值,若是,则满足仿真要求;若否,则不满足仿真要求。A parameter judging subunit, used for judging whether the error between the predicted scattering parameter of the microwave microstrip filter for predicting the scattering parameter and the target scattering parameter is less than the set threshold, if so, it meets the simulation requirements; if not, it does not meet the requirements Simulation requirements.
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