CN112231986A - Numerical control attenuator modeling method - Google Patents
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Abstract
The invention discloses a modeling method of a numerical control attenuator, which comprises the steps of obtaining test data of a reference state and an attenuation ground state, obtaining data of other attenuation states, preprocessing sample data, establishing a neural network model and training the neural network model. And then, a neural network model is constructed based on a BP neural network method by utilizing data in each working state, and the complex nonlinear mapping relation of the model can be accurately fitted. The numerical control attenuator modeling provided by the invention can perform self-adaptive training on input and output data to learn the behavior characteristics of the device without knowing the internal structure and working principle of a chip, and has obvious advantages compared with the traditional modeling method.
Description
Technical Field
The invention relates to the technical field of radio frequency/microwave modeling simulation, in particular to a modeling method of a numerical control attenuator.
Background
The radio frequency devices are various in types, structures are varied, working states and working principles are different, and the radio frequency devices can be divided into single-state devices and multi-state devices according to the working states of the devices. The numerical control attenuator controls the switching devices at all stages by utilizing codes, so that the branches of all attenuation modules are controlled to be in a straight-through state or an attenuation state, different working states are combined, and the numerical control attenuator is a multi-state chip as shown in figure 2.
The modeling of the radio frequency device is used as an important link of radio frequency/microwave modeling and simulation, and plays a very important role in accurately evaluating the electrical performance of a radio frequency microwave system, the transmission performance of signals and the like.
The traditional radio frequency chip modeling method mainly comprises the following steps: physical model method, equivalent circuit model method and numerical method. However, the physical model and the equivalent circuit model have fast calculation speed, but the internal structure and the working principle of the chip must be relatively known, and when the parasitic effect and the coupling effect are not deeply known, an accurate model is difficult to obtain; the numerical method needs huge calculation amount to obtain a more accurate model.
In addition, for a multi-state device, the input signal of the multi-state device also comprises a control signal besides a radio frequency signal, so that a plurality of working states can be combined, and the test work of the device and the model description of the device are more complicated.
Disclosure of Invention
Aiming at the limitations of the traditional modeling method and the characteristics of multiple states of the numerical control attenuator, the invention provides the numerical control attenuator modeling method, when the method obtains modeling sample data, all working states of the numerical control attenuator are not required to be tested, only a reference state (0dB attenuation state) and a plurality of attenuation ground states of the numerical control attenuator are required to be tested, and data of other working states are calculated through a de-embedding and cascading algorithm, so that the testing workload can be obviously reduced. And then, a neural network model is constructed based on a BP neural network method by utilizing data in each working state, and the complex nonlinear mapping relation of the model can be accurately fitted. The numerical control attenuator modeling provided by the invention can perform self-adaptive training on input and output data to learn the behavior characteristics of the device without knowing the internal structure and working principle of a chip, and has obvious advantages compared with the traditional modeling method.
In order to achieve the purpose, the numerical control attenuator modeling method comprises the following steps:
step one, obtaining reference state and attenuation ground state test data: aiming at the modeling of the numerical control attenuator, taking the S parameter as an output parameter of a neural network model and taking the frequency as an input parameter; carrying out data measurement on a reference state and each attenuation ground state of the numerical control attenuator by using a vector network analyzer, wherein the attenuation ground state is an attenuation state in which each attenuation module in the numerical control attenuator acts independently;
step two, acquiring data of other attenuation states: solving the S parameters of each attenuation module by a de-embedding formula, and then solving the cascade S parameters under different combinations of a reference state and a plurality of attenuation modules by using a cascade formula to obtain S parameter data under different working states;
step three, preprocessing sample data: carrying out reverse folding processing on periodically changed phase data in the S parameter, and carrying out normalization and random sequencing processing on a neural network training sample;
step four, establishing a neural network model: the data of the numerical control attenuator is autonomously learned by using a BP neural network algorithm to establish a model of the numerical control attenuator, the neural network model is respectively established aiming at S parameters of each dimensionality of the numerical control attenuator in each working state, and the number of layers of each neural network, the number of neurons of each hidden layer and an activation function of each neuron are determined;
step five, training a neural network model: and respectively training the neural network model according to the amplitude and the phase of each dimension S parameter of each working state.
Furthermore, in the first step, the amplitude and the phase of the S parameter of each channel are independently used as the output of the neural network for modeling so as to improve the accuracy and the training efficiency of the model, the model in each working state comprises a plurality of neural network models, and each neural network model only calculates and outputs one-dimensional S parameters.
Further, the reverse folding treatment in the third step comprises the following steps: when the phase data of one point is different from the phase data of the previous point by more than half of the phase period, the phase data of the current point is added or subtracted by a plurality of times of the period value, so that the change of the phase data is relatively smooth, and the correctness of the phase data is not influenced.
Furthermore, the normalization processing in the third step is to normalize the original data to the same scale range, so as to avoid that the absolute value difference of the physical quantities of all dimensions is too large to influence the model precision.
Further, a BP neural network model algorithm is adopted in the fifth step, the training process mainly comprises a forward calculation process and a reverse error propagation process, and the training process is carried out by utilizing a third-party neural network algorithm library.
Further, the neural network training process mainly needs to determine three parameters: training algorithms, learning rates, and iteration termination conditions.
The invention has the beneficial effects that:
according to the modeling method of the numerical control attenuator, modeling data of all other working states can be obtained through the de-embedding and cascading algorithm only by testing the reference state and the attenuation ground states, and the task load of device testing work can be effectively reduced. The invention accurately models the obtained modeling data by using a BP neural network algorithm, does not need deep knowledge of the internal structure and the working principle of a plurality of devices, and can quickly convert the modeling data into the neural network model of the numerical control attenuator without excessive prior knowledge. Based on the modeling method provided by the invention, the testing and modeling efficiency of the numerical control attenuator can be effectively improved.
Drawings
FIG. 1 is a flow chart of the numerical control attenuator modeling of the present invention;
FIG. 2 is a schematic diagram of the functional composition of the numerical control attenuator model of the present invention;
FIG. 3 is a schematic diagram of two-port device cascades and an equivalent network according to the present invention;
4-11 comparison curves of the output and test data of the neural network model with the S parameter of each dimension in the 9dB attenuation state model of the numerical control attenuator in the embodiment of the invention.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, specific embodiments of the present invention will now be described. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a modeling method of a numerical control attenuator, which comprises the following steps as shown in figure 1:
step one, obtaining reference state and attenuation ground state test data
Aiming at the modeling of the numerical control attenuator, the S parameter (the amplitude and the phase of the S parameter of each channel: S)11_amp,S11_pha，…Snn_amp,Snn_pha) As an output parameter of the neural network model, the frequency is used as an input parameter. It is worth mentioning that although the neural network model can be multi-output, in order to improve the model accuracy and training efficiency, the amplitude and phase of the S parameter of each channel are individually modeled as the output of the neural network, so that the model in each working state will includeA plurality of neural network models, each neural network model calculating and outputting only one-dimensional S parameters. Because the numerical control attenuator comprises a plurality of working states, each working state needs to be modeled separately.
As can be seen from fig. 2, the digitally controlled attenuator is composed of a reference state and a plurality of attenuation modules in cascade connection, and the combination of different attenuation states is realized by controlling the on-off of branches of each attenuation module through a switch. The attenuation state in which each attenuation module acts alone is referred to as an attenuation ground state, and other attenuation operating states can be formed by combining a reference state and different attenuation modules.
In the invention, data measurement is carried out on the reference state and each attenuation ground state only by using the vector network analyzer, and data of other attenuation states are obtained by de-embedding and cascading treatment, so that the task load of test work can be obviously reduced.
Step two, acquiring data of other attenuation states
The digitally controlled attenuator can be regarded as a two-port device, and various working states of the digitally controlled attenuator can be regarded as cascade connection of a plurality of two-port devices, as shown in fig. 3. Wherein the S parameter (S) after the cascadecomposite) Can be calculated from S parameters (S) of two devices before cascade connection(1)And S(2)) Calculated by a cascade formula. If S is knowncompositeAnd S(1)Then S can be obtained by de-embedding formula(2)。
From the above analysis, each attenuation ground state of the numerical control attenuator can be regarded as a reference state and a cascade of each attenuation module, and other attenuation states can be regarded as a reference state and a cascade of a plurality of attenuation modules. Therefore, the S parameter of each attenuation module is firstly solved through a de-embedding formula, and then the cascade S parameter under different combinations of the reference state and the plurality of attenuation modules is solved through a cascade formula, so that S parameter data under different working states are obtained.
Step three, preprocessing sample data
Preprocessing the sample data comprises the steps of performing anti-folding processing on periodically changed phase data and performing normalization and random sequencing processing on the neural network training sample, and the two steps of preprocessing operations are briefly described below.
1) Reverse folding operation
The S parameter includes periodically varying phase data (S)11_pha、S21_pha、S12_pha、S22_pha) For periodically changing phase data, a sharp mutation of the data can occur at a data point between two periods, which is represented by periodic folding of a data curve, and the accuracy of neural network modeling can be affected. In order to eliminate this effect, the periodic phase data needs to be processed by reverse folding, and the specific operation method is as follows: when the phase data of one point is different from the phase data of the previous point by more than half of the phase period, the phase data of the current point is added or subtracted by a plurality of times of the period value, so that the change of the phase data is relatively smooth, and the correctness of the phase data is not influenced.
2) Normalized, random sort operation
The data range is often very different because the input and output physical quantity units and dimensions are different. The normalization processing is to normalize the original data to the same scale range, so as to avoid influence on model accuracy caused by too large difference of absolute values of physical quantities of all dimensions.
The random ordering is to improve generalization ability of the neural network model and improve prediction accuracy of the model at input points outside the training samples, and the random ordering can be generally realized by program automation.
Step four, establishing a neural network model
The method utilizes the BP neural network algorithm to carry out autonomous learning on the data of the numerical control attenuator to establish an accurate model of the numerical control attenuator, and compared with the traditional modeling method, the method does not need to know the internal structure and the working principle of the numerical control attenuator and does not need excessive prior knowledge. And respectively establishing a neural network model aiming at the S parameter of each dimension of the numerical control attenuator in each working state, and determining the number of layers of each neural network, the number of neurons of each hidden layer and the activation function of each neuron.
Step five, training the neural network model
After the processing of the first step to the third step, the neural network model training can be carried out on each working state of the numerical control attenuator, and in order to improve the model precision and the training efficiency, the neural network model training is respectively carried out aiming at the amplitude and the phase of each dimensionality S parameter of each working state. The BP neural network model algorithm is adopted, the training process mainly comprises a forward calculation process and a reverse error propagation process, and the training process is usually carried out by utilizing a third-party neural network algorithm library. Three parameters are mainly determined in the neural network training process: training algorithm, learning rate, iteration termination condition.
In a preferred embodiment of the present invention, the above process is illustrated by taking a digitally controlled attenuator as an example, and comprises the following steps:
the method comprises the following steps: obtaining reference state and decay ground state test data
The numerical control attenuator in this embodiment is a 4-bit numerical control attenuator, and is composed of an 8dB attenuation module, a 4dB attenuation module, a 2dB attenuation module, and a 1dB attenuation module, which are combined to have 15 attenuation states and 1 reference state (0dB attenuation state, all attenuation module branches are disconnected). Respectively testing a reference state and 4 attenuation ground states (8dB, 4dB, 2dB and 1dB) of the numerical control attenuator by adopting a vector network analyzer, selecting 191 groups of samples with the test frequency of 1 GHz-20 GHz and the step of 100MHz, inputting sample data into each frequency point (f), and outputting corresponding S parameters (S) of each channel11_amp，S11_pha，S21_amp，S21_pha，S12_amp，S12_pha，S22_amp，S22_pha)。
Step two: obtaining data of other attenuation states
Through a de-embedding formula and a cascading formula, data in any state can be obtained by using reference state data and attenuation basic state data, and the process of obtaining data in any attenuation state is explained by taking data in a 9dB attenuation state as an example.
Because the 9dB attenuation state can be regarded as being formed by cascading the reference state and the 8dB and 1dB attenuation modules, and the 8dB and 1dB attenuation ground states both contain the reference state, the cascade connection is directly carried out, and the result error can be caused by cascading three reference states, therefore, firstly, the 8dB and 1dB attenuation ground states are respectively embedded with the reference states contained in the attenuation ground states by utilizing a de-embedding formula, and S parameters of the 8dB and 1dB attenuation modules are obtained; then, the reference state, the 8dB attenuation module and the 1dB attenuation module are respectively cascaded by utilizing a cascade formula to obtain cascade S parameters of the 9dB attenuation state.
Step three: preprocessing sample data
Preprocessing the obtained cascade S parameter of 9dB attenuation state, firstly, processing the phase data S with periodic variation11_pha、S21_pha、S12_pha、S22_phaPerforming reverse folding treatment by using a reverse folding formula; then, normalizing the input and output dimension S parameters by using a normalization formula respectively; then, randomly ordering the input and output dimension data, wherein the rules and the sequence of random ordering of the dimensions are consistent so as to ensure that the corresponding relation of input and output is unchanged; and finally, taking 80% of the preprocessed sample data as training samples and 20% of the preprocessed sample data as test samples, and directly applying the preprocessed sample data to training of the neural network model.
Step four: establishing a neural network model
The working states of the numerical control attenuator are respectively modeled, and the modeling process is still illustrated by taking 9dB attenuation state modeling as an example. And respectively establishing a neural network model aiming at each dimensionality S parameter of the 9dB attenuation state, giving an initial value of the neural network parameter, and then correcting when the neural network model is trained. The initial values of the network parameters given here are: the number of network layers is 3, wherein the network layers comprise an input layer, a hidden layer and an output layer; because the network input is frequency and the output is S parameter of a certain dimension, the number of neurons of the input layer and the output layer is 1, and the number of neurons of the hidden layer is set to be 20; the activation function of the hidden layer neuron selects a unipolar Sigmoid function, and the activation function of the output layer neuron selects a linear function.
Step five: training neural network model
Training the neural network model requires determining a training algorithm, an iteration termination condition and a learning rate. For this embodiment, the LM algorithm is selected as the training algorithm, the initial value of the learning rate is 0.3, the maximum number of iterations is 500, and the expected maximum mean square error is 10-7。
Here, the neural network model training of each dimension S parameter in the 9dB attenuation state model is still taken as an example to illustrate that, through multiple iterations and modification of corresponding parameters, a better parameter combination is finally obtained: the number of the network layers is 4, the number of the two hidden layer neurons is 15 and 50 respectively, the activation function of the hidden layer neurons is a unipolar Sigmoid function, the learning rate is 0.1, and other parameters are the same as the settings in the steps. The result comparison curve of each dimension S parameter neural network model output and test data is shown in figures 4-11, and the mean square error and the iteration number are shown in the following table:
in the embodiment, the modeling of the reference state and other attenuation states of the numerical control attenuator is similar to the modeling process of the 9dB attenuation states, and models of the numerical control attenuator in various working states can be obtained according to the steps.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A modeling method of a numerical control attenuator is characterized by comprising the following steps:
step one, obtaining reference state and attenuation ground state test data: aiming at the modeling of the numerical control attenuator, taking the S parameter as an output parameter of a neural network model and taking the frequency as an input parameter; carrying out data measurement on a reference state and each attenuation ground state of the numerical control attenuator by using a vector network analyzer, wherein the attenuation ground state is an attenuation state in which each attenuation module in the numerical control attenuator acts independently;
step two, acquiring data of other attenuation states: the other attenuation states are formed by combining the reference state of the numerical control attenuator with different attenuation modules, the S parameter of each attenuation module is calculated through a de-embedding formula, and then the cascade S parameter under different combinations of the reference state and the plurality of attenuation modules is calculated by utilizing a cascade formula so as to obtain S parameter data under different working states;
step three, preprocessing sample data: carrying out reverse folding processing on periodically changed phase data in the S parameter, and carrying out normalization and random sequencing processing on a neural network training sample;
step four, establishing a neural network model: the data of the numerical control attenuator is autonomously learned by using a BP neural network algorithm to establish a model of the numerical control attenuator, the neural network model is respectively established aiming at S parameters of each dimensionality of the numerical control attenuator in each working state, and the number of layers of each neural network, the number of neurons of each hidden layer and an activation function of each neuron are determined;
step five, training a neural network model: and respectively training the neural network model according to the amplitude and the phase of each dimension S parameter of each working state.
2. The method of claim 1, wherein in the first step, the amplitude and phase of the S parameter of each channel are individually modeled as the output of the neural network to improve the model accuracy and training efficiency, and each model in the working state comprises a plurality of neural network models, and each neural network model only calculates and outputs one-dimensional S parameters.
3. The method for modeling a numerical control attenuator of claim 1, wherein in step three, the reverse folding process comprises the steps of: when the phase data of one point is different from the phase data of the previous point by more than half of the phase period, the phase data of the current point is added or subtracted by a plurality of times of the period value, so that the change of the phase data is relatively smooth, and the correctness of the phase data is not influenced.
4. The method for modeling a numerical control attenuator according to claim 1, wherein in the third step, the normalization process is to normalize the original data to the same scale range, so as to avoid that the absolute value of the physical quantity of each dimension is too different to affect the model accuracy.
5. The modeling method of the numerical control attenuator according to any one of claims 1 to 4, wherein in the fifth step, a BP neural network model algorithm is adopted, and the training process mainly comprises a forward calculation and a reverse error propagation process and is performed by using a third party neural network algorithm library.
6. The method of claim 5, wherein the neural network training process essentially determines three parameters: training algorithms, learning rates, and iteration termination conditions.
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